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Jerzy Neyman (1894–1981)

Thu, 04/13/2017 - 7:00am
by Chin Long Chiang, Professor in the Graduate School, University of California, Berkeley


Jerzy Neyman, one of the principal architects of modern statistics, was Director of the Statistical Laboratory, University of California, Berkeley. He was born on April 16, 1894, into a Polish family in Bendery, Russia, and died on August 5, 1981, in Berkeley, California, at the age of 87. With Neyman’s passing, history closed a chapter on the early development of this important scientific field.

At the time of his birth, there was no Poland as a nation. “Poland proper” had been divided among Germany, Austria, and Russia. Neyman’s father was a lawyer. When Neyman was twelve years old, his father died of a heart attack. His caring mother moved her family to Kharkov, where he attended school and college. Although he was born a Pole, Neyman spoke Russian almost as early as he spoke Polish. At an early age, he could also speak Ukrainian, German, French, and Latin fluently. Upon his graduation from high school, through his mother’s arrangement, he joined a student group making a journey to see Europe outside Russia. Before entering the college in Kharkov, he decided to study mathematics instead of pursuing his father’s profession. He received his mother’s support and encouragement. “She had respect for intellectual activity,” Neyman fondly recalled to Constance Reid in the late 1970s. (Reid published her book entitled Neyman From Life in 1982.) In 1921, after a Polish-Soviet peace treaty, Neyman was sent to Poland in a repatriation of prisoners of war program between the two countries. Neyman saw his fatherland Poland for the first time when he was 27 years old.

Neyman’s interest in mathematics was reinforced when he studied with the Russian probabilist S. N. Bernstein at the University of Kharkov. When he read Henri Lebesgue’s Lecons sur L’intégration et la Recherche des Functions Primitives, Neyman was fascinated by sets, measure, and integration. During his college days he had proved five theorems on the Lebesgue integral on his own. His article entitled “Sur une théoréme metrique concernant les ensembles fermés,” published in 1923, was one of his early research papers in pure mathematics. His candidate thesis at the University of Kharkov (1916) was on the integral of Lebesque. In 1917, Neyman returned to the university for postgraduate study. In the following year he was a docent at the Institute of Technology, Kharkov. At the University of Warsaw, Neyman studied mathematics with Waclaw Sierpinski. He earned the Doctor of Philosophy degree from the University of Warsaw in 1924. The oral examination consisted of Rigorosum Major in mathematics and Rigorosum Minor in philosophy. No one knew more statistics than Neyman to examine him on the subject.

In the little spare time that he had during his student days, Neyman was heavily involved in teaching to earn a living. He also gave supplementary lectures for professors at the university and taught mathematics and statistics to college students.

Neyman first heard of Karl Pearson from reading Pearson’s book Grammar of Science (1892). Apparently, he was influenced by Pearson’s philosophical views as expressed in the book.

Neyman’s contact with statistics occurred early in his academic career. It appears that he had studied applications of mathematical statistics with Bernstein at the University of Kharkov. But he learned most statistics through work on his own, especially in agricultural experimentation. He held a position of “senior statistical assistant” at the National Agricultural Institute in Bydgoszcz, Poland, in 1921, and he was a special lecturer at the Central College of Agriculture in Warsaw in 1922.

In the fall of 1925, Sierpinski and Kazimierz Bassalik, the director of the National Agricultural Institute, were awarded a Polish Government Fellowship for Neyman to study mathematical statistics with Karl Pearson in London. Neyman was well prepared in mathematics and in statistics. While in London, Neyman and a young man about his own age, Pearson’s son Egon S. Pearson, became good friends.

During the academic year 1926–27, Neyman was on a Rockefeller fellowship to study pure mathematics in Paris. He attended lectures given by Emile Borel at the University of Paris and also lectures by Lebesgue and Jacques Hadamard at the College de France. In addition, he had some of his own notes read at these institutes. Quite possibly, the year of studying mathematics in Paris had prepared him well for his joint endeavor with Egon Pearson in the development of statistical theory in the years to come.

Neyman and Pearson’s joint work formally started in the spring of 1927, when Pearson visited Neyman in Paris. While there are no records of what transpired during the ten days during which they worked together, they must have laid out plans for their future joint project. At the end of the 1926–27 academic year, Neyman went back to Poland, and in 1928 he became head of the Biometric Laboratory at the Nencki Institute of Warsaw. He carried out his joint work with Pearson through correspondence between Warsaw and London. From 1928 to 1934, they published seven of their ten most important papers on the theory of testing statistical hypotheses.

In developing their theory, Neyman and Pearson recognized the need to include alternative hypotheses and they perceived the errors in testing hypotheses concerning unknown population values based on sample observations that are subject to variation. They called the error of rejecting a true hypothesis the first kind of error and the error of accepting a false hypothesis the second kind of error. They placed importance on the probability of rejecting a hypothesis when it is false. They called this probability the power of test. They proposed a term, ‘critical region’ to denote a set of sample statistical values leading to the rejection of the hypothesis being tested. The ‘size’ of a critical region is the probability of making the first kind of error, which they called the level of significance.

They called a hypothesis that completely specifies a probability distribution a simple hypothesis. A hypothesis that is not a simple hypothesis is a composite hypothesis. A hypothesis concerning the mean of a normal distribution with a known standard deviation, for example, is a simple hypothesis. The hypothesis is a composite hypothesis if the standard deviation is unknown.

It is now difficult for us to imagine how one could perform a statistical test without these concepts. But the Neyman-Pearson theory was a considerable departure from traditional hypothesis testing at the time. They were severely criticized for their new theory by the leading authorities of the field, especially by R. A. Fisher.

Neyman and Pearson used conceptual mathematics and logical reasoning to develop the theory of hypothesis testing. They emphasized “the importance of placing in a logical sequence the stages of reasoning in the solution of …inference.” In their initial papers (1928a) and (1928b), it seems that they were leading the reader, step by step, in their development of the theory. They relied on the concept of likelihood ratio in testing hypotheses concerning parameters in known probability distributions. And they elucidated their ideas further with specific examples and numerical computations.

After they had laid a solid mathematical foundation for their theory, they applied it to the problem of two samples (1930) and to the problem of k samples (1931). In one of their joint papers (1933) they used the likelihood ratio to establish an objective criterion for determining the best (in the sense of power of test) critical region for testing a simple hypothesis and a composite hypothesis. That was a high point of their accomplishment. The landscape of statistical hypothesis testing would no longer be the same.

In 1934, Neyman joined the faculty of E. S. Pearson’s Department of Applied Statistics at the University College London. From 1934 to 1938, they published only three more joint papers on testing hypotheses, possibly because of Pearson’s involvement in administrative responsibilities. Neyman, however, was still very productive during that period. From time to time, Neyman published papers on hypothesis testing on his own but most of the fundamental work was contained in his joint publications with Pearson.

When he was still in Poland, Neyman had developed the idea of confidence interval estimation. He even gave lectures on confidence interval estimation rather than hypothesis testing in his class at University College London in 1934. He published his work in 1937. At that time, many statisticians confused the confidence interval with the fiducial interval, a concept developed by Fisher. That confusion was soon dispelled by Fisher himself. Neyman clarified the difference between the two in his Lectures and Conferences (1938).

In addition to the theory of statistical inference, Neyman had made contributions to many other branches of statistics, such as the designs of agricultural experimentation (1923, 1925, 1935), the theory of sampling (1925, 1938, 1939), a class of ‘contagious’ distributions (1939), and others. He even used the “storks bring babies” example to show how to reach a wrong conclusion by misusing a correlation between variables, the so-called spurious correlation (1938).

Neyman’s work of applications of statistical methods in practical problems was very extensive. He considered practical problems as a source of inspiration for the theoretical statisticians.

There was an interesting feature in Neyman’s approach to practical problems. He had the ability to visualize the phenomena behind the data and a model of the mechanism that creates the phenomena. He would express the model in mathematical terms to produce new probability distributions, or new stochastic models. Only then would he find appropriate statistical methods to analyze the data on hand.

In the spring of 1937 Neyman delivered a series of lectures on mathematical statistics an probability at the Graduate School in the U.S. Department of Agriculture in Washington, DC. That was the first time that the American statistical public had the opportunity to hear statistical theory from Neyman in person. The lecture notes were subsequently published in 1937, and revised and expanded in 1952, under the title Lectures and Conferences on Mathematical Statistics and Probability. Among the reviews of the 1937 book, there was one written by William Feller, published in Zentralblatt, which reads in part as follows:

“The point of departure for the author is always actual practical problem and he never loses sight of the applications. At the same time his goal is always a truly rigorous mathematical theory. He appears to insist on absolute conceptual clarity and rigor, not only as a sound foundation, but also because it is really useful and necessary, particularly where the practical problem goes beyond the mathematical aspect…”

Feller’s words would apply equally well to Neyman’s other publications.

In 1938, Neyman accepted a mathematics professorship from the University of California at Berkeley. And he established the Statistical Laboratory, with himself as the director. That was the beginning of one of the preeminent statistical centers in the world. In 1955, Neyman established the Department of Statistics. He retained the title Director of the Statistical Laboratory.

Neyman was a very dynamic person, full of ideas and energy. Soon after the Statistical Laboratory was established and the teaching program was in good order, he began to plan a symposium of mathematical statistics and probability “to mark the end of the war and to stimulate the return to theoretical research.” The symposium had the participation of leading authorities in theoretical probability, in mathematical statistics, and in applied fields. The Proceedings of the symposium, edited by Neyman, were published in 1949 to “stimulate research and foster cooperation between the experimenter and the statistician.”

Success of the symposium prompted Neyman to plan a series of symposia, once every five years. The number of participants and the coverage grew from one symposium to the next. The Sixth Berkeley Symposium, held in three different periods in 1970 and 1971, was attended by 240 leading authors in 33 subject areas in theory of probability, in mathematical statistics, and in scientific fields with applications of statistics. The Proceedings, edited by LeCam, Neyman, and Scott, were published in 1972 in six volumes and 3397 pages—a gigantic undertaking.

These symposia supplemented the teaching programs and research academic activities normally carried out in universities and other academic institutions. They also had a great deal of influence on the attitude of theoretical statisticians and research scientists, making them recognize the need and the advantage of applications of statistics.

During the forty years that he was in Berkeley, Neyman had students come from all over the world to attend his lectures and to learn the proper way of conducting research. Neyman was a generous man. He helped students financially in any way he could. He recommended students for University scholarships and he secured federal grants for the support of students and faculty. At times, when he could not obtain the funds he needed to support students from any other sources, Neyman took the money out of his own pocket.

Neyman used to say “Statistics is the servant to all sciences.” In many ways Neyman had expanded the domain and improved the quality of the service.

Related Links

Jerzy Neyman”, School of Mathematics and Statistics, University of St. Andrews, Scotland

What Does Susan Hilsenbeck Do When She Is Not Being a Statistician?

Sat, 04/01/2017 - 7:00am
This column focuses on what statisticians do when they are not being statisticians. If you would like to share your pastime with readers, please email Megan Murphy, Amstat News managing editor.


Susan Hilsenbeck

Who are you, and what is your statistics position?

My name is Susan Hilsenbeck, and I am a professor of medicine and the leader of the Biostatistics and Informatics Shared Resource in the Dan L. Duncan Comprehensive Cancer Center at Baylor College of Medicine in Houston, Texas. I spend most of my time working collaboratively with investigators on cancer research.

Tell us about what you like to do for fun when you are not being a statistician.

When I am not working as a biostatistician, I like to scuba dive and I like to fish, but most of all I like to quilt.

Susan Hilsenbeck scuba dives.

What drew you to this hobby, and what keeps you interested?

I’ve been quilting on and off since 1982, which means I started shortly after the ‘quilt renaissance’ of the 1970s. I got started because I watched a program on PBS and I wanted to make a present for a friend having a baby. Even for my first quilt, I aimed for something original, although I am a strong believer in mastering good technique and then branching out to make it your own.

I work pretty slowly and savor every aspect of the process. I’ve made very few bed-sized quilts and focus instead on baby-sized and wall art quilts.

One of the things I love about quilts is how the gift of a quilt expresses love and regard. The experience of making a group quilt is especially fun and can help build bonds of friendship. Over the last 17 years, since joining Baylor, my co-workers and I have made more than 30 lap/crib-sized group quilts to celebrate life events like babies, marriages, retirements, etc. The group quilts are usually more whimsical in style, while my solo quilts are more on the experimental and art side (or at least I like to think so).

Susan Hilsenbeck included a DNA molecule applique in this quilt she designed.

The thing I like best about quilting—as a mode of expression—is the blend of analysis/engineering to figure how to put something together; the physical skill needed to execute the design; and the freer, artistic side of design, composition, color, etc. It seems like the perfect mix for all sides of my brain. There is also something incredibly relaxing and centering about spending a couple of hours focused on stitching. I keep at it because there is always more to learn and the possibilities are endless.

To view some of Hillsenbeck’s work, visit her blog.

2017 Data Challenge Sees 16 Contestants

Sat, 04/01/2017 - 7:00am

The ASA’s Statistical Computing, Government Statistics (GSS), and Statistical Graphics sections are sponsoring the 2017 Data Challenge, which will take place at the Joint Statistical Meetings in Baltimore.

The goal of the contest, which began earlier this year with analyses of the Bureau of Labor Statistic’s Consumer Expenditure Survey, is to challenge participants to analyze a government data set using statistical and visualization tools and methods. Of the college students and professionals who submitted an analysis, 16 were chosen to present their results in a speed poster session at JSM. All JSM attendees are encouraged to view the presentations.

There will be two award categories: professional (one level) and student (three levels). These awards will be announced at the GSS general membership meeting.

The sections are also moving forward with a special issue of Computational Statistics, which will feature refereed articles from contestants in the 2016 Data Challenge. The data set for the 2016 challenge came from the Department of Transportation’s General Estimates Systems. For the 2017 challenge, the plan is to publish selected refereed articles in a special issue of the Monthly Labor Review.

Master’s Programs in Data Science and Analytics

Sat, 04/01/2017 - 7:00am
More universities are starting master’s programs in data science and analytics due to the wide interest from students and employers. Amstat News reached out to the statistical community involved in such programs. Given their interdisciplinary nature, we identified those that involved faculty with expertise in different disciplines to jointly reply to our questions. In 2015, for example, the ASA issued a statement about the role of statistics in data science, saying statistics is one of three foundational disciplines of data science. While the ASA has not issued a statement about the role of statistics in analytics, we assume statistics to also be foundational there. For this reason, we highlight the programs that are cross-disciplinary and engage statisticians. We will publish responses over a few issues of Amstat News. ~ Steve Pierson, ASA Director of Science Policy


University of Tennessee

Robert Mee is the William and Sara Clark Professor of Business, Department of Business Analytics and Statistics, Haslam College of Business, University of Tennessee. He is an ASA fellow who earned his PhD in statistics from Iowa State University.


Master’s in Business Analytics

Year in which first students graduated: 2011
Number of students currently enrolled: 38 full time, 6 part time. In the fall semester, approximately 80 (first- and second-year combined)

How do you view the relationship between statistics and data science?
Statistics informs both data collection and analysis. Other disciplines are involved with acquiring, managing, and analyzing data, but statistics gives particular attention to potential biases in both collection of data and in estimates of models. Statistics has the tools for quantifying uncertainty, understanding sources of variation, and confirming or contradicting hypotheses. Data science is centered on data and algorithms, as opposed to statistics, which begins with a problem to be addressed.

Describe the basic elements of your data science curriculum and how it was developed.
The core MSBA curriculum combines statistics, data mining/machine learning, optimization, database, and other computing skills, as well as giving the students a foundational understanding of the problems businesses address with analytics. Our students can choose electives in statistics, customer analytics, supply chain analytics, machine learning, or computer science to prepare them for their intended career direction.

We include a business perspective in our curriculum by maintaining close relationships with members of our Business Analytics Forum. In addition, many MSBA faculty members have extensive consulting and executive MBA teaching experience.

What was your primary motivation(s) for developing a master’s data science program? What’s been the reaction from students so far?
In 2009, our visionary department head brought an IBM white paper on the future of analytics to the attention of faculty. The paper emphasized applied statistics, business intelligence, and process optimization. At that time, we had two parallel master’s programs, one in statistics and the other in management science. We decided to combine these programs, creating a business analytics MS that now includes applied statistics, data mining, optimization, and Big Data tools.

What types of jobs are you preparing your graduates for?
Data scientist, analytics consultant, data analyst for supply chain or marketing. The title business analyst is not quite suitable, since this is often the title intended for less-quantitative MBA graduates.

What advice do you have for students considering a data science degree?
Take engineering calculus and learn some programming language. Pursue business analytics if you want to solve quantitatively-oriented problems and enjoy working with vast amounts of data to produce actionable insights that affect a business’s bottom line.

The letter “T” is sometimes used to characterize the business analytics masters, with the top of the T reflecting the breadth of this interdisciplinary degree and the vertical part indicating depth in one technical area. A statistics or computer science MS typically would have greater depth, but would lack the breadth of an MSBA.

Describe the employer demand for your graduates/students.
Last December, we graduated our 6th class of MSBA students. Through 2015, we have had 100% placement within three months of graduation. Companies making three or more hires include Amazon, The Boeing Company, Eastman Chemical Company, Hanesbrands Inc., Home Depot, and Regal Entertainment Group. We have also had many graduates work for consulting companies, including Accenture, Deloitte, EY, McKinsey & Company, KPMG, PWC, and several smaller firms.

Do you have any advice for institutions considering the establishment of such a degree?
Know your competitors. Consider the target incoming students, as this determines the length of the program. Consider your strategic advantages, especially ties with industry.

George Mason University

Robert Osgood is the director of the data analytics engineering program. He has expertise in developing and applying analytics to law enforcement in digital forensics, enterprise case management, cyber crime, counterintelligence, information security/technology, team leadership, and critical infrastructure protection.


Daniel Carr is professor of statistics and director of the statistics concentration in the data analytics engineering program. His driving interest is to create statistical graphic designs and software to address constraints posed by human cognition and challenges posed by new kinds of data and large data sets.


Master’s in Data Analytics Engineering

Year in which first students expected to graduate: 2014
Number of students currently enrolled: 290
Partnering departments: Volgenau School of Engineering (Lead): Statistics, Systems Engineering and Operations Research, Information Sciences and Technology, Computer Science
Program format: The MS Data Analytics Engineering (DAEN) program is an in-person program, although some courses are offered online. Our student body is a mix of full-time and part-time, both domestic and international. We are actively involved with our corporate partners and career services unit to offer internships for students.

Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.
The MS DAEN program is interdisciplinary. It revolves around a 15-credit core component with a 15-credit concentration component for a total of 30 credits.

Concentrations include applied analytics, business analytics, data mining, digital forensics, health care analytics, predictive analytics, and statistics. The core component consists of four courses, each taught in different departments, and a capstone.

Our admission criteria vary by concentration, but a minimum of one semester each of calculus, programming, and statistics is required. For the statistics concentration, three semesters of calculus, linear algebra, and probability are required.

What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?
The Volgenau School of Engineering at Mason, through working with its corporate partners and its advisory board, identified the need for data analytics education. Our investigation showed there was a growing demand for individuals with data analytics skills. Northern Virginia is a particularly appropriate area for employment in data analytics.

Student reaction has been extremely positive. Our spring 2017 enrollment of 290 students shows a definite demand for data analytics knowledge.

How do you view the relationship between statistics and data science/analytics?
There are two main components to data analytics: computing technology and statistics. Statistical analysis is what drives meaning from the massive data sets deposited with computing technology (hardware and software). The statistical component also includes visualization and data reduction.

What types of jobs are you preparing your graduates for?
Students are obtaining positions in a varied array of industries: cyber security, finance, government, information and knowledge management, software development, and e-commerce. Wherever data are collected, the need for analysis and statistical techniques is present.

What advice do you have for students considering a data science/analytics degree?
All departments (disciplines) look at the world from a certain point of view. Data analytics is not just computer science, or statistics, or business processes. It’s all of the above. So students looking for a more interdisciplinary view must seriously consider data analytics versus one of the traditional degrees.

It needs to be pointed out that data analytics requires more than just novice knowledge of com-puter science and statistics. For example, students need a solid statistical foundation normally found in a typical undergraduate statistics class. Also, knowledge of probability is also quite helpful. A minimum of one semester of calculus is also critical. Students need programming expertise. Any language will work, but Python is a particularly valuable language. Knowledge of database design and interaction is also desirable. Development of communication skills is essential in dealing with interdisciplinary stakeholders.

With these core components in place, a student can leverage his/her data analytics learning experience at Mason to the fullest extent.

Describe the employer demand for your graduates/students.
Our interaction with our corporate partners and exit survey data show a significant demand for Mason graduates. The finance, government, and technical sectors have employed our data analytics graduates.

Do you have any advice for institutions considering the establishment of such a degree?
The program must be interdisciplinary. No one owns data analytics, but everyone uses data analytics, so everyone needs to be a stakeholder. At Mason, the department of statistics is housed in the engineering school, along with the department of computer science, department of information sciences and technology (IT management), and department of systems engineering and operations research (predictive analytics). While each department created its own concentration with its own set of prerequisites, a common set of core courses was developed with minimal prerequisites, and each department contributed a course to that core.

University of Minnesota

Cavan Reilly earned his PhD in statistics at Columbia University in 2000, after which he joined the faculty in biostatistics at the University of Minnesota, where he has remained. Over the last several years, he has transitioned to working on more applied problems with an emphasis on clinical research on infectious diseases.


Dan Boley is professor of computer science and director of the graduate studies for master’s of science in data science program at the University of Minnesota. His research interests include computational methods in linear algebra, scalable data mining algorithms, algebraic models in systems and evolutionary biology, and biochemical metabolic networks.


Master’s in Data Science

Year in which first students expected to graduate: 2017
Number of students currently enrolled: 34
Partnering departments: Computer Science and Engineering (Lead), Statistics, Public Health (Division of Biostatistics), Electrical and Computer Engineering
Program format: Combination/ (distance learning option available)/typically traditional full-time, but a part-time option is available. Assistantships are available, but not guaranteed. Thirty-one credit hours required; six credit hours over two semesters are for a cumulative research project.

Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.
Our data science curriculum is intended the fill the spaces between algorithmics, statistical analysis, and modern computing infrastructure (these are the three core areas of our program). Finding the appropriate balance between these components and not simply duplicating and re-labeling existing opportunities for students was our guiding principle.

The curriculum was developed jointly by faculty from computer science and engineering, the school of statistics, electrical engineering and computer science, and the division of biostatistics in the school of public health. Through a series of meetings open to anyone interested, a consensus emerged that our program would focus on providing students with rigorous training in statistical methodology combined with a practical focus on computational feasibility in the age of Big Data, informed by a contemporary understanding of the possibilities engendered by the latest developments in hardware.

To distinguish our degree from the more business-oriented degree more commonly offered by many institutions around the country (including ours), we opted for a rigorous degree demanding a solid background in computing (3–4 semesters) and math/stat (3–4 semesters) at the undergraduate level as a minimum requirement. The other essential ingredient in our degree is a research component consisting of a two-semester capstone project.

What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?
This degree was created to meet the demand from companies for practitioners with solid training in scalable computing methods combined with a solid understanding of statistical issues and methods. Individual programs already had solid curricula in place to address various components of data science, but it was hard for students to package a program with the necessary elements without a cross-disciplinary degree. Many students in all contributing programs were already trying to assemble plans of study that would provide them with the necessary expertise one would expect from a degree in data science; this program has formalized that training. The response from students thus far has been very positive.

How do you view the relationship between statistics and data science/analytics?
We see statistics as but one component of a well-balanced program in data science. Some classical statistical techniques that served science well in the 20th century are simply not up to the task of dealing with data sets from the 21st century. This has led to extensive cross-fertilization between topics traditionally viewed as more within the realm of computer science and electrical engineering than statistics and ideas of great interest to statisticians (e.g., machine learning and causal inference). While such developments are positively affecting the practice of statistics and computer science, there are still opportunities to advance science that require perspectives and skills beyond what is possible in the context of a statistics curriculum or a computer science curriculum. As such, we see the relationship as complementary.

What types of jobs are you preparing your graduates for?
This is a new program, but our students have found internships at various companies employing sophisticated technology in the area, including health insurance, retail, and major social media companies on the coasts.

What advice do you have for students considering a data science/analytics degree?
Such students should strive for a balance between the three core areas identified above during their undergraduate education. Less balance is appropriate for a student specializing in computer science or statistics. The usual calculus sequence and linear algebra are still essential. A year of probability and statistics and a year of data structures and algorithms are becoming prerequisites. An internship is always helpful.

If a student is interested in graduate-level training in a field involving machine learning, data analytics, artificial intelligence, or data mining, having a solid computing background will be essential to implement anything novel. However, a solid statistics background will be essential to ensure that whatever is implemented gives statistically reliable predictions. Many employers have realized that a computing background alone is not sufficient for their next level of data systems development.

Do you have any advice for institutions considering the establishment of such a degree?
Ensure there is no obvious path for students to accomplish the same Stat/CS curriculum through an existing degree (unless you are prepared to simply re-label the existing degree). Make a choice between a regular graduate program with a choice of courses from a short list of requirements on the one hand and a cohort program where all students take the same courses together in sync.

Bentley University

Mingfei Li is an associate professor at Bentley University, where she has been a faculty member since 2008. She is currently serving as the MSBA program director and coordinator of the business analytics certificate and concentration programs. Her research interests include health analytics and sequential predictions.


Master of Science in Business Analytics

Year in which first students graduated: 2016
Number of students currently enrolled: 142 for the MSBA degree, 95 for a business analytics certificate or concentration within other degrees
Partnering departments: Mathematical sciences
Program format: Bentley University is a private business university broken down into a school of business and a school of arts and sciences. The master of science in business analytics (MSBA) is the only graduate degree offered by a department (mathematical sciences) not in the school of business. The department of mathematical sciences provides the quantitative curriculum for the MSBA degree, as well as all the other analytic courses offered at Bentley in statistics, mathematics, quantitative finance, data mining, Big Data, and operations research. Consequently, the department of mathematical sciences is, by necessity, interdisciplinary.

Currently, our program is an in-person program, but has synchronous hybrid classes for some of the courses. The MSBA degree requires 30 credits, and many courses require student projects. We have both full-time and part-time students. Bentley also provides scholarship and assistantship for some outstanding students.

Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.
Our program has introductory statistics as a program prerequisite. Students are not required to have coding/programming skills upon entry because we offer programming classes from our curriculum: SQL, data science for R programming, reporting and data visualization, Java programming, HTML, Hadoop, MapReduce introduction, etc.

The MSBA has six required classes: SQL, operations research, and four business-oriented applied statistics classes. Students can choose four elective classes from a list, which includes classes from disciplines representing areas of business applications such as computer science, marketing, finance, economics, management, and informational process management. We collaborate with other departments on these elective classes.

For topics like database development and management, we use courses offered by our computer science colleagues. For business context, we use courses offered by departments of the school of business such as finance, marketing, management, economics, and information management. So while the MSBA program is highly interdisciplinary across both schools in our university, the core focus on analytics led to the decision that the MSBA be managed in the mathematical sciences department.

Beyond the existing curriculum, we continue to develop additional analytical courses to enrich the program curriculum such as machine learning (using R and Python) and design of experiments for business.

What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?
Bentley began to offer analytic courses in the 1990s. In 2006, we began to offer a certificate program in business analytics in response to a strong demand for graduates with comprehensive skills centered on statistics and including computer sciences skills, business knowledge, and communications skills. With strong demand from both students and employers, we launched the MSBA degree program in 2013.

Our enrollment rapidly increased from our first class of 40 to the current 162 students. Applications have increased in a similar manner.

How do you view the relationship between statistics and data science/analytics?
Applied statistics is the core of both data science and analytics. Understanding data and knowing how to analyze data are essential in most of applications. Computer science knowledge and programming skills are necessary to facilitate most analyses. Understanding the business questions and stakeholders’ interests provide the guide to statistical thinking and planning for analysis. Therefore, in our program, we require that students develop competency and skills in all three areas: analytics (statistics and operations research), computer science (related to data management and computing), and business (context for application).

What types of jobs are you preparing your graduates for?
Our graduates are hired by companies across different sectors, both private and nonprofit. Because of the wide applicability of business analytics, graduates have a variety of job titles such as business system analyst, data scientist, analytics consultant, data analytics adviser, research analyst, senior modeling analyst, business analyst officer, and business analyst. Employers of our students include CVS, National Grid, Toys R Us, Accenture, Deloitte, Ernst and Young, EMC, and Boston Children’s Hospital.

What advice do you have for students considering a data science/analytics degree?
Compared to a computer science degree or a traditional statistics degree, Bentley’s MSBA is a degree that integrates applied statistics with computer science, operations research, and business knowledge. Through this degree, students get interdisciplinary analytic knowledge with a practical understanding of business. Graduates can work with both business and technical teams to be a problem solver and innovator, providing decision support and business insights.

Describe the employer demand for your graduates/students.
There is a strong demand from employers for our MSBA students. From a survey of our first MSBA graduating class (88% response rate), 94% of graduates got full-time jobs within 90 days of graduation. The average annual salary for these graduates was $78,000, and the median salary was $80,000.

Do you have any advice for institutions considering the establishment of such a degree?
Being an interdisciplinary program, the MSBA needs support from multiple academic and administrative departments. Communication is crucial for the coordination and management of the program, as well as for curriculum development. A designated program director has been critical to coordinate all aspects of the program—from admissions to advising to placement—and to work directly with faculty colleagues, administrators, and students.

Students enrolling in the MSBA program have varied backgrounds, interests, and future goals. It is challenging to accommodate this degree of student variation in the curriculum, academic advising, and career services. The program director interacts with students personally throughout their enrollment in the program to understand their individual background and interests and advise their course selections and career preparation to help students achieve their personal goals.

Meet Hubert Hamer: NASS Administrator

Sat, 04/01/2017 - 7:00am
Amstat News invited Hubert Hamer—administrator of the National Agricultural Statistics Service—to respond to the following questions so readers could learn more about him and the agency he leads.

A graduate of Tennessee State University, Hubert Hamer once served as director of the Statistics Division of the USDA, which produces and releases more than 400 national statistical reports each year covering the agency’s crops, livestock, economic, demographic, environmental, and census programs. He also served as executive director of the NASS Agricultural Statistics Board and executive director of the Advisory Committee on Agriculture Statistics.


What about this position appealed to you?

It has always been a goal of mine to provide leadership for the organization I grew up in. I’ve always loved and understood the importance of our mission to provide timely, accurate, and useful statistics in service to agriculture. I think it is rare that one has the opportunity to build a career in something meaningful that one really cares about. I feel very fortunate that I have been able to do just that by combining agriculture and statistics. By working my way up through the ranks at NASS in the Washington, DC, headquarters and in offices around the country, I’ve learned from my colleagues, supervisors, and mentors in NASS and USDA. I’m honored to be able to apply that knowledge and experience to lead this agency.

Describe the top 2–3 priorities you have for the National Agricultural Statistics Service.

Service and our commitment to U.S. agriculture are what drive us at NASS. To that end, I have three areas of focus:

  • Relationships with survey respondents
  • NASS employees
  • Advancing our use of technology to ensure data quality and usability

In my first months as administrator, I have made it a priority to get out and meet with staff, stakeholders, and respondents across the country. My purpose for these meetings is to strengthen the dialog and relationships to remain relevant and provide the outstanding products and services the agriculture community and others expect from us.

In turning to our staff, my goal is to have an environment in which our outstanding employees can do their best work. This includes a safe and inclusive workplace with diversity of people and ideas; where vigorous, respectful debate is encouraged; and where employees can continue to harness their talent and work ethic to fulfill both their career goals and our agency mission. My expectation is that everyone who interacts with NASS staff finds us to be helpful, pleasant, and professional.

In the area of technology, we have amazing tools to help us that I couldn’t imagine using even just a few years ago. I think George Washington, one of the earliest compilers of agricultural data, and those who later produced the first census of agriculture in 1840 would be astounded. Like other statistical organizations, we are working hard to efficiently and effectively harness geospatial tools, a wide range of available data, the internet, the cloud, data visualization, and many other constantly evolving assets to enhance data collection, analysis, and dissemination. To me, technology is our future.

What do you see as your biggest challenge(s) for NASS?

Probably the three greatest challenges for us today are reflected in my priorities. I believe our greatest challenges are keeping up with expanding data needs, reversing the trend of declining response rates that many survey-based organizations are experiencing, and bringing along a next generation of statisticians.

Starting with the topic of meeting data needs … with interests as varied as research and education, community-based planning, farm-related marketing, commodity markets, and the Farm Bill, we are constantly looking forward. For example, in the past few years, we’ve expanded our portfolio of publicly available data to include topics as diverse as grain crushing, flour milling, local foods, organic production, and the cost of pollination. We’ve also created a host of new types of data products. On obtaining sufficient response to surveys, I see this as a real challenge and an opportunity. We are reinvigorating our relationships with farm organizations to see if they can help us reach out to the farmers and ranchers from whom we request information. This has been a very positive effort on many levels.

Finally, one of the great things about NASS is our sense of family. Like me, many come out of college and stay here for their whole career. We have a large number of people who have been in the agency for decades and are starting to retire. We have a renewed focus on recruiting young people, training them, and keeping them so we maintain our knowledge and top-notch skill base.

What kind of support from the statistical community do you look for?

The statistical community has always been an incredible resource for sharing experiences, technologies, and best practices, as well as for looking ahead for new developments. We are also a great support for each other, which I always find valuable. A couple of areas I expect we’ll be focusing on together are to understand and address the reasons for declining response rates, educate decision makers and stakeholders across disciplines about the value of data as a public resource, and collaborate on and use new technologies that enhance our data-collection and dissemination practices, so we all remain relevant.

Prior to your tenure, what do you see as the biggest recent accomplishment of the agency?

This may sound routine, but maintaining our schedule of releasing some 450 reports a year on time and without errors. It is truly a testament to our staff’s commitment, especially while keeping up with new technologies and learning to collect data on new topics from farmers, ranchers, and agricultural businesses they may never have worked with before.

And along those lines, of course, it is a major achievement to conduct a successful Census of Agriculture every five years and create a portfolio of new customer-centric data products. We’ve been working since the release of the 2012 Census of Agriculture to get ready for the 2017 data collection, which will begin late November with a mail out to 3.1 million farmers and ranchers. We are really excited about a new online survey data-collection tool that we expect to use the first time for the census. We are testing it now and have great hopes that respondents will find it to be a convenient and flexible way to fill out the Census of Agriculture.

CSP 2017 Brings Statisticians Face to Face

Sat, 04/01/2017 - 7:00am

Tim Hesterberg gives a short course.

Sara Burns (winner of the Bartko scholarship) with John Bartko

Bei-Hung Chang presents her poster at the Opening Mixer.

Curtin Award winner Jami Jackson Mulgrave presents her poster at the Opening Mixer.

Roland Albert Matsouaka, of Duke University, and Peter Hanging Zhang, of Otsuka Pharmaceutical Development and Commercialization, Inc.

David Banks gives the keynote address.

The audience listens to David Banks during the keynote presentation.

Hadley Wickham answers a question from Sara Burns during his short course, “Expressing Yourself with R.”

Photos courtesy of Meg Ruyle/ASA

Moon Jung Cho, Bureau of Labor Statistics and CSP Program Chair


The 2017 Conference on Statistical Practice was held February 23–25. Four hundred fifty participants gathered at the Hyatt Regency Jacksonville Riverfront in Jacksonville, Florida. 

The conference began with short courses and an opening mixer on Thursday. The following two days were filled with presentations, poster sessions, tutorials, and practical computing demonstrations. Keynote speaker David Banks set the tone of the conference with his presentation “Snakes and Ladders: Challenges in Forging a Career in Statistics.”

Unique to CSP is its theme, “Communication, Collaboration, and Career Development.” This theme provides participants with tools for leadership and external communication, with the goal of empowering participants to bring a positive impact to their organizations.

A new feature this year was a face-to-face task group charged with facilitating networking for conference attendees that allowed them to engage and connect with each other easily. Some of the face-to-face ideas included the following:

    SPEED NETWORKING: We set up speed networking tables at the poster session Friday evening. Participants were assigned a table for a quick round of speed networking to meet and discuss the conference. 

    COMMON AREA GATHERING SIGNS: Gathering signs were placed in the common area to encourage participants to have meeting points prior to going for coffee, lunch, dinner, and other conference-related events.

    DINNER FOR CONSULTANTS: There was a dinner for aspiring and practicing statistical consultants. The goal was to make personal and professional connections and get support from colleagues.

    THEME DINNERS: Participants signed up for topic-based group dinners on Thursday and Friday evenings. The “Data for Good” theme dinner was led by David Corliss; “Misleading Graphs” by Naomi Robbins; and “Organizational Impact, Analytics Change, and Soft Skills for Success” by Terri Henderson.

    EVENING OUTING: Around 30 people participated in a Jacksonville “Legends and Liars” walking tour led by a local historian and storyteller Friday evening.

As in previous years, we continued the CSP Mentoring Program, which was designed to establish a 1:1 mentoring relationship between junior and senior statistical practitioners and provide an opportunity to enhance personal and professional development goals.

By design, the CSP space is centered on a common area that holds the exhibitor booths, opening mixer, poster sessions, and breakfast and any refreshments. This physical arrangement, along with the small conference size, provides a great opportunity for continuous networking throughout the conference.

The CSP 2017 Best Student Poster Award went to Thomas Metzger of Virginia Tech for “Detecting Interaction in Two-Way Unreplicated Experiments via Bayesian Model Selection” and Carl Ganz of UCLA Center for Health Policy Research for “Using Shiny to Efficiently Process Survey Data.”

CSP 2018 will take place in Portland, Oregon. We hope to see you there!

Why Be an Independent Consultant?

Sat, 04/01/2017 - 7:00am
This column is written for anyone engaged in or interested in statistical consulting. It includes articles ranging from what starting a consulting business would entail to what could be taught in a consulting course. If you have ideas for articles, contact the ASA’s Section on Statistical Consulting publication’s officer, Mary Kwasny.

Stephen Simon is a part-time independent statistical consultant and part-time faculty member in the department of biomedical and health informatics at the University of Missouri-Kansas City. He writes about statistics, evidence-based medicine, and research ethics.


So, you want to be an independent statistical consultant? Hang out your shingle and start helping people who come your way? Give up all the security that comes with consulting within a larger organization?

Are you crazy?

Inside a large organization, you have a support network. You have a human resources department that can help you update your insurance coverage when you get married. You probably have access to an administrative assistant who can help you prepare expense reports. That large organization will support your professional development, paying your way to the continuing education course at the Joint Statistical Meetings.

Independent consulting is not a job for the timid. But if you like being in control, it’s the best job in the world.

I like to write books and articles, and I haven’t figured out yet how to bill any of this work to a particular client. That’s true in some large organizations as well, but many places do offer time and support for professional activities that are not directly tied to a particular client.

In a large organization, if you don’t know how to run a mediation analysis, you can walk down the hall to a colleague’s office to ask a few questions. You also don’t have to go out and find customers, because your customers are down the hallway from you, as well.

At a larger organization, you will have a boss and, for all we like to gripe about bosses, they can often be a great benefit to your career. They review your work and make suggestions on how to do better. They pick the work assignments they think will help you grow and become more valuable to your organization. They counsel you when you have problems.

Most importantly, if you’re part of a larger organization, your paycheck and working hours stay constant during busy and quiet times. In contrast, it’s feast or famine in the world of independent consulting.

As an independent consultant, you end up doing a lot that isn’t really statistical in nature and may take you outside your comfort zone. I work with an accountant, but I’ve had to learn a lot more about accounting than I expected to. I’m the sort of person who, if the amount in your checkbook register is within a hundred dollars of what the bank says you have, thinks the difference must be sampling error.

It’s not just accounting. I pay someone to do my taxes, but when I became an independent consultant, the amount of paperwork I had to pull together for him by April 15 tripled.

You can get legal advice on the best type of business entity to set up, but this is your business, so you will still need to understand the fundamental differences between a sole proprietorship, limited liability corporation, S corporation, and C corporation. Even if your lawyer reviews your contracts, you will end up reading them in detail before you sign off.

Most importantly, you don’t have an organization that finds work for you. So, if you don’t market yourself properly, you won’t have any clients, you won’t make any money, and you will starve to death.

And yet, if you’re the right person, you’d be crazy not to consider a career as an independent consultant. When you are your own business, you have a level of control that is liberating. You don’t like a particular client? You have the option of just walking away. It’s a loss of income, for sure, but some clients are not worth any amount of income. If you try to walk away from a client at a larger organization, your boss needs to okay it first. Nine times out of 10, you will get so much grief about not being a “team player” that it won’t be worth it.

Independent consulting is indeed spread irregularly, but even during busy times, you still have a lot of control over when and where you do your work. That was one of the biggest attractions for me. I have the option of going on field trips with my son and attending all of his track meets. I can be home with him when he’s sick and take him to all his doctor’s appointments. And when he sleeps until 2 p.m. on weekends, that’s when I get much of my work done.

As an independent consultant, you do have to pay for your own continuing education, but the nice thing (beyond it being a tax deduction) is that you don’t have to justify to anyone other than yourself that it’s about time you learned how to run all these new Bayesian models.

That support network in a larger organization? It’s not always there, to be quite honest, and you can build your own support network as an independent consultant. I volunteered to step in as president of the Kansas City R Users Group, and beyond the exposure and the number of new clients it has brought me, the other members of this group have been invaluable resources for things like version control software, data mining, and text analytics.

Independent consulting is not a job for the timid. But if you like being in control, it’s the best job in the world.

BEA’s Innovation Spurs Projects for Richer Economic Statistics

Sat, 04/01/2017 - 7:00am

Brian Moyer oversees the Bureau of Economic Analysis’ production of official economic statistics, which provide a comprehensive, up-to-date picture of the U.S. economy that aids in decision making by businesses, policymakers, and households. He holds a PhD in economics from American University.

To continue capturing a full and detailed picture of a dynamic, $18 trillion-plus economy, the people of the Bureau of Economic Analysis (BEA) have to be economic data pioneers. They are committed to innovating and exploring, whether the bureau is enhancing existing statistics or creating new ways to measure the U.S. economy. That mind-set is crucial to delivering on BEA’s mission: producing the timeliest, relevant, and accurate economic statistics for the American public in an objective and cost-effective manner.

Here is a snapshot of a few of the data projects BEA’s economists are working on.

The Digital Economy. We are moving forward on a three-pronged plan to better measure fast-changing technologies and their effect on the U.S. economy. One focus is refining price measurements to better capture innovations in high-tech goods and services such as software, cellphones, personal computers, computer servers, cloud computing, and medical imaging equipment.

To tackle improvements in quality-adjusted price measures for such products, BEA is doing the following:

  • Conducting an in-house review of GDP and its components to identify areas in which existing quality-adjusted prices could be improved or new indexes could be introduced
  • Partnering with source data agencies, including the Federal Reserve Board and Bureau of Labor Statistics, to improve software and medical equipment price measurement
  • Engaging experts for specialized research such as building new price indexes for cloud computing

In addition, BEA economists are developing a roadmap to define and measure the digital economy. BEA is researching how to more accurately measure the impact of information technology on the overall U.S. economy and how to improve the measurement of digitally enabled commerce.

Third, BEA is researching the economic impact of “free” entertainment such as Facebook apps and internet games, which are largely supported by advertising revenue. And BEA is committed to better understanding the impact of technology-enabled, peer-to-peer access to goods and services—typically referred to as the “sharing economy.”

Health Care. Created in 2015, our new set of health care statistics break out spending by the treatment of disease, such as circulatory diseases or cancers, rather than by the place of service, such as a hospital or doctor’s office. Each year, BEA plans to release a fresh batch of health care statistics, building a longer time series. Data are currently available for 2000 through 2013. Figures for 2014 will be released later this year.

These data offer new insights into health care, which accounts for about 18 percent of the U.S. economy. After years of research, BEA created a “blended account,” which combines data from multiple public and private sources, including large claims databases covering millions of enrollees and billions of claims.

In its next steps, BEA plans to research linking changes in the costs of treating diseases to improvements in the quality of treatments, including advances that lead to better health outcomes. That’s one of the biggest challenges in precisely measuring medical spending and prices—not unlike what BEA is confronting in the high-tech sector.

BEA also plans to build a detailed input-output framework for health care spending, giving users a way to better analyze the production of goods and services by health care industries. BEA will incorporate prices that reflect the costs of treating diseases into the input-output framework.

There’s More. We have other innovative data projects in the wings, including laying the groundwork for a new set of statistics—a small business satellite account. It would measure, for the first time, the size and health of a sector that’s often at the leading edge of risk-taking, entrepreneurship, and economic growth in the United States.

BEA is also exploring the feasibility of measuring economic growth in the nation’s 3,000-plus counties. These first-of-their-kind BEA statistics would help businesses identify local markets for their products, assist local governments seeking to attract investment, and give a fuller picture of the U.S. economic landscape.

On the global front, BEA is working to expand statistics to provide a more detailed look at how businesses buy and sell services around the world. Quarterly statistics on U.S. trade in services will be expanded to cover 90 countries and country groups (from the current 38.) Details will be published about some of the most dynamic sectors, including research and development, intellectual property, and medical services.

Data Tools. We are also creating new ways to access our data. The newest offering makes data available through the bea.R Library, an open-source data tool for users of the statistical programming language “R.” This gives users a quick way to access our economic statistics, requiring only a few lines of code to do so. BEA’s data also is available through its application programming interface (API), interactive data tables, and other data tools at the BEA website.

Biometrics Section Prepares for JSM 2017

Sat, 04/01/2017 - 7:00am
Edited by Zheyu Wang, Biometrics Section Publications Officer

Want to get more involved in the Biometrics Section? Interested in contributing articles to the Biometrics Section newsletter? Contact the section’s publication officer, Zheyu Wang.

JSM 2017 Program

The Biometrics Section will sponsor the following continuing education (CE) courses at the 2017 Joint Statistical Meetings in Baltimore:

    Longitudinal and Incomplete Data
    Instructor(s): Geert Molenberghs and Geert Verbeke

    An Introduction to the Joint Modeling of Longitudinal and Survival Data, with Applications in R
    Instructor(s): Dimitris Rizopoulos  

    Bayesian Designs for Phase I-II Clinical Trials
    Instructor(s): Peter Thall and Ying Yuan

    Regression Modeling Strategies
    Instructor(s): Frank Harrell

    Precision Medicine Through Optimal Treatment Regimes
    Instructor(s): Eric Laber, Marie Davidian, Anastasios (Butch) Tsiatis, and Shannon Holloway

    Statistical Analysis with Missing Data
    Instructor(s): Roderick Little and Trivellore Raghunathan

    Analysis of Categorical Data
    Instructor(s): Christopher Bilder and Thomas Loughlin

To read about the 2017 section award winners, visit Biometrics Section news.

Celebrate the Significance of Mathematics and Statistics

Sat, 04/01/2017 - 7:00am
April is Mathematics and Statistics Awareness Month


April marks a time to increase the understanding and appreciation of mathematics and statistics. Why? Because both subjects play a significant role in addressing many real-world problems—climate change, disease, sustainability, the data deluge, internet security, and much more. Research in these and other areas is ongoing, revealing new results and applications every day in fields such as medicine, manufacturing, energy, biotechnology, and business. Mathematics and statistics are important drivers of innovation in our technological world, in which new systems and methodologies continue to become more complex.

Be sure to check the ASA’s website throughout April for contests and activities!

“Because of the massive increase in the amount of data available, and because of the important contributions of statistics to making sense of the data, statisticians are in hot demand,” said Ron Wasserstein, the ASA’s executive director. “Jobs in statistics command good pay, have great working conditions, and allow individuals to solve problems that make a difference to the world.”

In the age of Big Data, statistics underlies almost every decision made today, whether it’s the effectiveness of a new drug or treatment or the debut of a mobile device. Statistics is how analysts convert raw data into useful information, from studies of proteins to surveys of galaxies.

Research in statistics and the mathematical sciences is important for its applications and because it trains one in critical thinking and problem solving. From magic squares and Möbius bands to magical card tricks and illusions, mysterious phenomena with elegant “Aha!” explanations have been part of both subjects for centuries.

This month, let’s celebrate mathematics and statistics and the diverse researchers and students in these fields who are contributing so much to furthering discoveries, solving problems, and finding beauty in our world.

Mathematics and Statistics Awareness Month is a program of the Joint Policy Board for Mathematics (JPBM)—a collaborative effort of the American Mathematical Society, the American Statistical Association, the Mathematical Association of America, and the Society for Industrial and Applied Mathematics.


If you would like an 11 x 17 printed copy of the poster mailed to you, email the ASA’s communication manager, Megan Murphy.

New AMS Blog Covers Under-Represented Groups in Mathematics

Sat, 04/01/2017 - 7:00am

A new American Mathematical Society (AMS) blog—called “inclusion/exclusion” debuted recently. The blog will cover issues pertaining to marginalized and under-represented groups in mathematics.

The editor-in-chief, Adriana Salerno of Bates College, and editors Edray Goins of Purdue University, Brian P. Katz of Augustana College, Luis Leyva of Vanderbilt University, and Piper Harron of the University of Hawaii at Manoa hope the blog will help develop a more inclusive, supportive, and diverse community of mathematicians.

The first posts are titled “Inclusion/Exclusion Principle,” “Hidden Figures: How and Why We Brought It to the 2017 JMM,” and “Hands Off My Confidence.” Future topics may include conferences targeted to under-represented groups; inclusive teaching strategies; summaries of current educational research; profiles of inspiring and successful under-represented mathematicians; and advice for students, faculty, and researchers at all levels.

The AMS invites readers to subscribe to the blog to receive notifications of new posts by email. Also, join the conversations by posting comments.

Consultant’s Corner: A New Column for Consultants

Wed, 03/01/2017 - 7:03am
This column is written for anyone engaged in or interested in statistical consulting. It includes articles ranging from what starting a consulting business would entail to what could be taught in a consulting course. If you have ideas for articles, contact the ASA’s Section on Statistical Consulting publication’s officer, Mary Kwasny.

Contributing editor Mary Kwasny is an associate professor in the department of preventive medicine and an active member of the Biostatistics Collaboration Center at Northwestern University, Feinberg School of Medicine. She has been enjoying the art of statistical consulting and collaboration for more than 20 years in academic medical centers and external non-profits.

What does a publications officer of a section do? Admittedly, there are differences in sections, and while most sections have a newsletter, many sections also have a newsletter editor in addition to a publication’s officer. This is true for the Section on Statistical Consulting.

So, when I was fortunate to be elected the publications officer of the Statistical Consulting Section, I was not really sure what the job entailed. The Section’s charter states the following:

The publication’s officer shall coordinate paper and electronic publications associated with the section including, but not limited to, section columns in Amstat News, proceedings of meetings, and other presentations, but excluding the section newsletter…. When requested by the editors, the publications officer shall assist in soliciting, reading, and editing articles on statistical consulting for publication in the association’s journals.

Sure enough, the institutional memory of the position seemed to allow a lot of leeway in my interpretation of the job—and anyone who knows me, knows I have a lot more faith in the spirit, if not the letter, of the law. So I thought if the function of the newsletter was to communicate the “goings on” of the section to the section, then the publications officer should be responsible for communicating those “goings on” to the greater ASA. So, what “goings on” would we communicate with the greater ASA and how?

Since ASA Connect launched, it has been clear to me that some sections are very active and some not so much. The Section on Statistical Consulting has an incredibly active discussion board; it was primarily that section that led me to ask how to change my settings to a daily digest, rather than real time. The discussions range from starting a consulting business to whether insurance for that business is a good idea, from how best to predict which clients might have projects that take much longer than the client or even the consultant might expect to advantages and disadvantages of billing at intervals or at project’s end. There was a very active debate when the definitions of consultants and collaborators were contrasted. Needless to say, I believe there are many “goings on” of the section that might appeal to the greater ASA audience.

I do not have a master’s degree, but I truly enjoy reading the Master’s Notebook. I was curious to see if there could be a corner of Amstat News concerned with “all things consulting,” akin to the Master’s Notebook. Chuck Kincaid, the current chair of the section, and I pitched the idea, and we got a go ahead to try it! So, be on the lookout for the Consultant’s Corner!

We are excited to launch this idea next month, with articles ranging from what starting a consulting business would entail to issues that could be taught in a consulting course to other issues that statistical consultants might face. My guess is there are many great ideas for best practices of consulting, as well as many great stories about consults that have made an impact on the world.

If you have ideas for articles (questions you would like answered about consulting practice or your own stories), please forward them to me at I will happily liaison between the readers and the section so this corner may be a great way to encourage, enlighten, and entertain.

ASA Statistics Poster Competition for Grades K–12

Wed, 03/01/2017 - 6:00am

The ASA/NCTM Joint Committee on Curriculum in Statistics and Probability and the ASA’s education department encourage students and their advisers to participate in its annual poster competition.

What is a statistical poster? A statistical poster is a display containing two or more related graphics that summarize a set of data, look at the data from different points of view, and answer specific questions about the data.


Posters must measure between 18 and 24 inches high and 24 and 30 inches wide.

Any weight of paper is permitted.

The best way to send the poster is flat, between taped sheets of cardboard. Do not send posters rolled in a tube. Between 200 and 400 posters are entered, so send posters using a method that lends itself to easy opening with a razor. No extra papers, “peanuts,” or other non-Earth-friendly packing materials should be included.


First Prize – $300, a plaque, and a plaque for the school
Second Prize – $200 and a plaque
Third Prize – $100 and a plaque
Honorable Mention – A plaque

If the submission is a collaborative effort, the prize money will be divided equally.

Also, first-place winners in grades 4–12 will receive Texas Instruments graphing calculators. First-place winners in grades 4–6 and their advisers will receive TI-73 Explorer Graphing Calculators. The winners in grades 7–12 and their advisers will receive TI-84 Plus Silver Edition Graphing Calculators.

Be sure posters are not wrapped so securely that opening them becomes a challenge. Do not use duct tape or large amounts of tape.

Any layers of paper on posters must be affixed securely.

Posters must be the original design and creation of the entrant(s).

Computer graphics may be used.

Subject matter is the choice of the participant(s) or their classmates.

An example of the original data and brief descriptions of the method of collection and purpose of the experiment must be taped securely to the back of the poster. (Cite references for published data.)

In submitting a poster, students agree that the poster may be displayed at the ASA’s Joint Statistical Meetings, featured in its publications, and included on its website.

All entries become the property of the ASA and cannot be returned.

Only first-, second-, and third-place winners and honorable mentions will be notified personally. The ASA website will announce winners in August.

Students may work individually or in teams. For those in the K–3 category, there is no restriction on the size of the team. For other categories, the maximum number of students per team is four. For teams with members from different grade levels, the highest grade determines the entry category.


Teachers and statisticians, whose decisions are final, will judge the posters on the following:

  • Overall impact of the display for eye-catching appeal, visual attractiveness, and its ability to draw the viewer to investigate the individual graphs (more than one graph is required for all but the K–3 category)
  • Clarity of the message’s demonstration of important relationships and patterns, obvious conclusions, and ability to stand alone, even without the explanatory paragraph on the back
  • Appropriateness of the graphics for the data
  • Creativity

There is no entry fee, but your poster must be postmarked by April 1 and sent to Poster Competition, 732 North Washington Street, Alexandria, VA 22314-1943.

For details, visit the ASA’s website.

Jerome Sacks Award

Wed, 03/01/2017 - 6:00am

The National Institute of Statistical Sciences (NISS) is seeking nominations for the 2017 Jerome Sacks Award for Outstanding Cross-Disciplinary Research. The prize recognizes sustained, high-quality cross-disciplinary research involving the statistical sciences.

An award of $1,000 will be presented during the NISS/Statistical and Applied Mathematical Sciences Institute reception at the Joint Statistical Meetings in Baltimore, July 29 – August 3, 2017.

To nominate an individual, submit as one PDF document the following information to by May 1:

  1. Nomination letter (maximum two pages)
  2. Supporting letters from two individuals (other than nominator)
  3. The nominee’s CV

For more information and to see the list of previous winners, visit the NISS website.

Technometrics Highlights: Latest Issue Covers Design, Analysis, Anomaly Detection

Wed, 03/01/2017 - 6:00am

Volume 59, Issue 1 of Technometrics includes 11 articles covering topics ranging from design and analysis of complex, black-box computer simulations to algorithmic design approaches for customizing and enhancing key properties of physical experiments to anomaly detection in image and other high-dimensional data streams.

In the paper titled “Monotonic Metamodels for Deterministic Computer Experiments,” author Matthias Hwai Yong Tan explores the challenging goal of incorporating prior knowledge that the response is monotonic in some of the input variables in deterministic computer simulations. Although the Gaussian process (GP) models ubiquitously used for simulation response surface modeling are not monotonic, incorporating such information can substantially improve the accuracy and interpretability of the response predictions. Previous methods that project GP sample paths onto some space of monotonic functions fail to preserve important GP modeling properties such as the prediction uncertainty shrinking at locations close to the design points. This paper develops a weighted projection approach that more effectively uses information in the GP model, together with two computational implementations. The first is isotonic regression on a grid, while the second is projection onto a cone of monotone splines, which alleviates problems encountered in a grid-based approach. Simulations show the monotone B-spline metamodel gives particularly good results.

In “Sliced Full Factorial-Based Latin Hypercube Designs as a Framework for a Batch Sequential Design Algorithm,” Weitao Duan, Bruce E. Ankenman, Susan M. Sanchez, and Paul J. Sanchez develop a method for more efficiently fitting complex models such as finite element or discrete event simulations. To reduce experimental effort, sequential design strategies allow experimenters to collect data only until some measure of prediction precision is reached. The authors’ batch sequential experiment design method uses sliced full factorial-based Latin hypercube designs, which are extensions of sliced orthogonal array-based Latin hypercube designs. At all stages of the sequential design, their approach achieves good univariate projection properties, and the structure of their designs tends to produce uniformity in higher dimensions, which results in the excellent sampling and fitting properties the authors demonstrate with empirical and theoretical arguments.

In “Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion,” Xu He, Rui Tuo, and C. F. Jeff Wu address the problem of Gaussian process-based optimization for multi-fidelity deterministic computer experiments having tunable levels of accuracy. They propose an optimization scheme that sequentially adds new computer runs based on two sampling criteria. Their first expected quantile improvement criterion scores the desirability of candidate inputs for a fixed accuracy level of the simulator, and their second expected quantile improvement efficiency criterion scores the desirability of candidate combinations of inputs in conjunction with simulator accuracy level. The latter allows not only the inputs, but also the simulator accuracy level, to be strategically chosen for the next round of simulation. Their approach is shown to outperform the popular expected improvement criterion.

In “Calibration of Stochastic Computer Simulators Using Likelihood Emulation,” Jeremy E. Oakley and Benjamin D. Youngman combine simulation and physical experimental data in the so-called calibration problem, which involves modeling the difference or discrepancy between physical reality and its imperfect representation embodied by the simulation. Their focus is on stochastic computer simulation models in which each run takes perhaps one or two minutes. They combine a Gaussian process emulator of the likelihood surface with importance sampling, such that changing the discrepancy specification changes only the importance weights. One major benefit of this is that it allows a range of discrepancy models to be investigated with little additional computational effort, which is important because it is difficult to know the structure of the discrepancy in advance. The approach is illustrated with a case study of a natural history model that has been used to characterize UK bowel cancer incidence.

In “Design and Analysis of Experiments on Non-Convex Regions,” Matthew T. Pratola, Ofir Harari, Derek Bingham, and Gwenn E. Flowers present a new approach for modeling a response in the commonly occurring but under-investigated situation in which the design region is non-convex, for which current tools are limited. The authors’ new method for selecting design points over non-convex regions is based on the application of multidimensional scaling to the geodesic distance. Optimal designs for prediction are described, with special emphasis on Gaussian process models, followed by a simulation study and an application in glaciology.

In “Nonstationary Gaussian Process Models Using Spatial Hierarchical Clustering from Finite Differences,” Matthew J. Heaton, William F. Christensen, and Maria A. Terres consider the modeling of large spatial data having nonstationarity over the spatial domain, which is frequently encountered in science and engineering problems. The computational expense of Gaussian process modeling can be prohibitive in these situations. To perform computationally feasible inference, the authors partition the spatial region into disjoint sets using hierarchical clustering of observations with finite differences in the response as a measure of dissimilarity. Intuitively, directions with large finite differences indicate directions of rapid increase or decrease and are, therefore, appropriate for partitioning the spatial region. After clustering, a nonstationary Gaussian process model is fit across the clusters in a manner that allows the computational burden of model fitting to be distributed across multiple cores and nodes. The methodology is motivated and illustrated using digital temperature data across the city of Houston.

The next three papers develop tools that advance the design and analysis of physical experiments by harnessing modern computational capabilities. In “Benefits and Fast Construction of Efficient Two-Level Foldover Designs,” Anna Errore, Bradley Jones, William Li, and Christopher J. Nachtsheim further substantiate recent arguments that small foldover designs offer advantages in two-level screening experiments. In addition, the authors develop a fast algorithm for constructing efficient two-level foldover designs and show they have superior efficiency for estimating the main effects model. Moreover, their algorithmic approach allows fast construction of designs with many more factors and/or runs. A useful feature of their compromise algorithm is it allows a practitioner to choose among many alternative designs, balancing the tradeoff between efficiency of the main effect estimates vs. correlation and confounding of the two-factor interactions.

In “Two-Level Designs to Estimate All Main Effects and Two-Factor Interactions,” Pieter T. Eendebak and Eric D. Schoen investigate the related problem of designing two-level experiments large enough to estimate all main effects and two-factor interactions. The effect hierarchy principle often suggests that main effect estimation should be given more prominence than the estimation of two-factor interactions, and orthogonal arrays favor main effect estimation. However, recognizing that complete enumeration of orthogonal arrays is infeasible in many practical settings, the authors develop a partial enumeration procedure and establish upper bounds on the D-efficiency for the interaction model based on arrays that have not been generated by the partial enumeration. Their optimal design algorithm generates designs that give smaller standard errors for the main effects, at the expense of worse D-efficiencies for the interaction model, relative to D-optimal designs. Their generated designs for 7–10 factors and 32–72 runs are smaller or have a higher D-efficiency than the smallest orthogonal arrays from the literature.

In “Joint Identification of Location and Dispersion Effects in Unreplicated Two-Level Factorials,” Andrew J. Henrey and Thomas M. Loughin relax the assumption that the location effects have been identified correctly when estimating dispersion effects in unreplicated factorial designs, violation of which degrades the performance of existing methods. The authors develop a method for joint identification of location and dispersion effects that can reliably identify active effects of both types. A normal-based model containing parameters for effects in both the mean and variance is used and parameters are estimated using maximum likelihood with subsequent effect selection via a specially derived information criterion. The method successfully identifies sensible location-dispersion models missed by methods that rely on sequential estimation of location and dispersion effects.

“Anomaly Detection in Images with Smooth Background via Smooth-Sparse Decomposition,” by Hao Yan, Kamran Paynabar, and Jianjun Shi, tackles the emerging problem of how to analyze high-dimensional streams of image-based inspection data for process monitoring purposes. In manufacturing applications such as steel, composites, and textile production, anomaly detection in noisy images is of special importance. Although several methods exist for image denoising and anomaly detection, most perform denoising and detection sequentially, which affects detection accuracy and efficiency, in addition to being computationally prohibitive for real-time applications. The authors develop a new approach for anomaly detection in noisy images with smooth backgrounds. Termed smooth-sparse decomposition, the approach exploits regularized high-dimensional regression to decompose an image and separate anomalous regions by solving a large-scale optimization problem. Fast algorithms for solving the optimization model are also developed.

In “Estimation of Field Reliability Based on Aggregate Lifetime Data,” Piao Chen and Zhi-Sheng Ye present an approach for fitting distribution models to failure data that are aggregated (with substantial loss of information) in a particular way that is common in reliability databases for complex systems with many components. Instead of individual failure times, each aggregate data point is the sum of a series of collective failures representing the cumulative operating time of one component from system commencement to the last component replacement. This data format differs from traditional lifetime data and makes statistical inference challenging. The authors consider gamma and inverse Gaussian distribution models and develop procedures for point and interval estimation of the parameters, based on the aggregated data.

Biometrics Section News for March

Wed, 03/01/2017 - 6:00am
Edited by Zheyu Wang, Biometrics Section Publications Officer

    The Biometrics Section is looking for volunteers to help chair a session at this year’s JSM. Chairing a session is an important responsibility and a great way to meet your colleagues. If you are interested, contact our section’s 2017 Program Chair, Barbara Engelhardt.

    Interested in Getting More Involved?

    Want to get more involved in the Biometrics Section? Interested in contributing articles to the Biometrics Section newsletter? Contact the section’s publication officer, Zheyu Wang.

    Strategic Initiatives Grant Awardees

    The Biometrics Section is pleased to announce that the following three proposals have been funded as part of the section’s strategic initiative, “Developing the Next Generation of Biostatisticians”:

    • Stacia DeSantis, The University of Texas School of Public Health, “Developing the Next Generation of Biostatisticians: Leveraging NIH Training Grant Recipients to Perform Outreach in Texas”
    • Lillian Prince, Kent State University, “Biostatistics and Research Awareness Initiatives Network, Inc. (BRAIN)”
    • Kristen McQuerry, University of Kentucky, “Inspiring the Next-Generation Biostatistician”
    2017 Award Winners

    The David P. Byar Young Investigator Award is given annually to a new researcher in the Biometrics Section who presents an original manuscript at the Joint Statistical Meetings. The award commemorates David Byar, a renowned biostatistician who made significant contributions to the development and application of statistical methods during his career at the National Cancer Institute. In addition, the section gives travel awards. This year, we had 52 submissions to the paper competition. We are pleased to announce the following recipients:

    David P. Byar Young Investigator Award

    Edward Kennedy, Carnegie Mellon University, “Robust Estimation and Inference for the Local Instrumental Variable Curve”

    Travel Awards 
    • Joseph Antonelli, Harvard T.H. Chan School of Public Health, “Double Robust Matching Estimators for High-Dimensional Confounding Adjustment”
    • Qingpo Cai, Emory University, “Bayesian Variable Selection Over Large-Scale Networks via the Thresholded Graph Laplacian Gaussian Prior with Application to Genomics”
    • Anqi Cheng, University of Washington, “Monotone Distribution Function Estimation in Randomized Trials with Noncompliance”
    • Wenting Cheng, University of Michigan, “Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information”
    • Chanmin Kim, Harvard T.H. Chan School of Public Health, “Bayesian Methods for Multiple Mediators: Relating Principal Stratification and Causal Mediation in the Analysis of Power Plant Emission Controls”
    • Shelley H. Liu, Harvard T.H. Chan School of Public Health, “Lagged Kernel Machine Regression for Identifying Time Windows of Susceptibility to Exposures of Complex Metal Mixtures”
    • Krithika Suresh, University of Michigan, “Comparison of Joint Modeling and Landmarking for Dynamic Prediction Under an Illness-Death Model”
    • Guan Yu, State University of New York at Buffalo, “Optimal Sparse Linear Prediction for Block-Missing Multi-Modality Data Without Imputation”
    • Xiang Zhan, Fred Hutchinson Cancer Research Center, “A Fast Small-Sample Kernel Independence Test with Application to Microbiome Association Studies”

    2016–2017 Academic Salary Survey

    Wed, 03/01/2017 - 6:00am

    The 2016–2017 academic salary survey includes both faculty and nonfaculty statisticians and biostatisticians. We received responses from 59 institutions in the United States. The data included 1,034 faculty and 149 nonfaculty statisticians, with gender information. The quartiles and 90th percentile for relevant categories are provided in the summary tables.

    Note: The number of categories for “Professor” has been reduced this year to get more stable and reliable summary statistics. Those interested may request the tables using previous years’ categories.

    Faculty Data

    The faculty data set, comprised of 679 males and 355 females, included faculty members in 25 statistics departments (N=473), 20 biostatistics departments (N=412), and 17 math sciences departments (N=149).

    Table 1 summarizes salary information for full-time academic faculty in statistics departments by rank and years in rank, based on a nine-month salary. Table 2 provides similar information for full-time academic faculty in biostatistics departments, but is based on a 12-month salary. Table 3 summarizes salary information on full-time academic faculty in the mathematical sciences departments by rank, based on a nine-month salary. A few cases of statistics and mathematical sciences faculty with 12-month salaries were adjusted down by a factor of one-fourth, and a few cases of biostatistics faculty with nine-month salaries were adjusted up by a factor of one-third. Tables 4, 5, and 6 provide similar percentiles for the groups in Tables 1, 2, and 3, respectively, stratified by gender. Tables 8, 9, and 10 were added this year to provide salary information by tenure status.

    Nonfaculty Data

    The nonfaculty data set included 149 observations from 24 institutions, with 37 at the doctoral level and 112 at the master’s level. Of the 149 individuals, there were 120 from biostatistics departments, 28 from statistics departments, and one from mathematical sciences. Table 7 provides their salary distribution, stratified by highest degree (master’s or doctorate) and years since earning the highest degree.

    2018 ASA Board of Directors Candidates

    Wed, 03/01/2017 - 6:00am

    The ASA announces the selection of candidates for the 2017 election. The winning candidates’ terms will begin in 2018. Make sure to look for your ballots in your email inbox and vote early. Voting begins at 12:01 ET March 15 and ends at 11:59 p.m. PT on May 1. Complete candidate biographies can be read on the ASA website.

    Running for President-Elect
      David L. Banks

      Professor of the Practice, Department of Statistical Science, Duke University

      I have been fortunate to have had a checkered career. It has exposed me to a wide range of statistical activity, mostly at universities and federal agencies. But my first job out of college was doing statistical analyses for a government contractor, which is how I learned what I wanted to be.

      David L. Banks

      From graduate school forward, the ASA has been a presence in my professional life. I joined in 1980, attended my first Joint Statistical Meetings in 1982, and am happy to have been at every JSM since Philadelphia in 1984. The ASA is my community, and the friendships I have found within it have enriched my life.
      The American Statistical Association is 177 years old. It is a social machine built by generations of statisticians to achieve two purposes: to advance our profession and to advance our careers. In terms of the first goal, the ASA has had many successes—it has distinguished statisticians from mathematicians, enabled and empowered the federal statistical agencies, and brought statistical thinking into the high-school curriculum.

      But challenges change. I believe the hurdles ahead are to ensure that public policy is based upon data, rather than politics, that we strategically redefine our relationship with the emerging data science community, and that we help the general citizenship to see us as somewhat cooler and a bit more trustworthy then they presently do.

      In terms of the second goal, the ASA has been strikingly successful in fostering careers. Compared to many other fields, we are, on median, well compensated and enjoy high levels of job satisfaction. But we need to do more to raise the floor. And, since careers at different stages use the ASA’s assets in different ways, we need to clue in junior colleagues on how the ASA can help leverage professional growth.

      One of the joys of our profession is that, compared to other sciences, we are relatively diverse in gender and employment (besides academics and industry, we are prominent in government). We must work to build that out more. If elected as ASA president, I would use the office to further our field and to help others advance.

      Statistics has pivoted from mathematics toward applications (we are a big tent, and there will always be need for deep theory, but our world is bigger than that). We must provide capacity to support that change. For example, I believe MS and PhD students (and everyone else) should have easier opportunities to learn the modern heavy-lifting Big Data programming languages, such as Spark. The ASA can help that happen. Also, I believe that our publication system no longer efficiently serves our science—I have an extended rant on this that appeared in Amstat News #424—and I shall urge the ASA to modernize.

      Those who know me know that I would be an active president. The office is a once-in-a-lifetime opportunity to work with some of the best people on the planet to make our profession stronger. I could never waste that “chance.”

      Karen Kafadar

      Chair and Commonwealth Professor, Department of Statistics, University of Virginia

      “… [A]s new discoveries are made, new truths discovered, and manners and opinions change, with the change of circumstances, institutions must advance also to keep pace with the times.” ~ Thomas Jefferson

      Karen Kafadar

      I am pleased to run for ASA president at a time when our strategic plan goals—enhancing diversity and breadth of our association, increasing visibility of our profession, and ensuring our future—are as urgent as they have ever been. But the “circumstances” that drive our goals have changed: We face complex challenges and we “must advance also to keep pace with the times.”


      Our profession faces threats in many fields (data science, psychology, economics, bioinformatics) whose training may include casual brushes with statistics. This has created populations of self-proclaimed statisticians who can sideline us in critical research areas unless we actively change our approach to statistical education and our response to society’s needs to be more relevant to today’s demand for solutions to complex multifaceted problems.
      Complex problems—such as detecting emerging epidemics, ensuring food safety, protecting our communications, and establishing reliable standards—cannot be solved by single individuals. More urgently, we need to anticipate these needs before others capitalize on our delay and develop attractive, but flawed, approaches. These challenges require diverse talents that include domain scientists and the best statistical solutions from statisticians whom we attract to our field and prepare to face big problems.

      The ASA must create ways to forecast these tsunamis of change, identify our present and future statisticians to address them, and assist our members in developing data-based solutions that require our statistical expertise.

      Teams for Complex Problems

      These challenges present opportunities to promote our profession and to grow our field, both in numbers of statisticians and in the nature and quality of research solutions that define us. In my experience, statisticians have been critical components of teams that address problems in academe, industry (HP), and government (NIST, NCI). All too often, this involvement arises by serendipity. Two examples are the 2009 NAS [National Academies of Science] report on forensic science and the IPCC [Intergovernmental Panel on Climate Change] climate change reports; on both, the ASA reinforced statisticians’ roles by keeping the topics on congressional and media radar screens. We need to develop further mechanisms to both forecast areas of change and respond to them while we continue to build on our past successes.

      Engaging Our Sections and Chapters

      We can start by mobilizing the talent in our sections and chapters. In education, many ASA chapters have active connections with their local schools and universities. Our future ASA members will come from diverse populations only if they can identify with those they see today. The [ASA] Board and I can work with ASA chapters to support good role models on their outreach visits to local colleges, elementary [schools], and high schools. Next, experts in sections’ disciplines such as the sciences, health care, and computing can easily identify critical problems within their domains. The ASA can mobilize teams to match these problems with scientific and statistical expertise and initiate mechanisms to tackle them.

      The Next Statistical Frontiers

      The age of self-contained problems solved by analyzing a few data sets is rapidly being replaced by global challenges that span multiple disciplines whose diverse data demand a myriad of methodological approaches. We must design mechanisms so our engagement on important complex problems is more likely and timely than mere serendipity, and then deliver results. I am honored by the opportunity to serve the ASA as president, and will work with the diversity of expertise and talent in our association to develop ways to better enable us to identify, mobilize, support, and encourage our members to work on important problems and, in so doing, learn from all of you as well.

        Running for Vice President
          Lorraine Denby

          Principal, Murray Hill Data Science

          My history within the ASA has been that, when elected to an office, I have created initiatives that make a lasting mark on our profession, raised the funds as needed, and carried them out to fruition. Some of these initiatives are still in place today. I would be grateful to have this opportunity again as your vice president.

          Lorraine Denby

          For example, you may not know the history of As representative to the Council of Sections in the mid-90s, I decided that it was time for the ASA to have a web presence. I approached the board and got its approval, but no financial assistance. I then approached each section for a donation toward the project, raised over $30k, formed the committee, and voila! was born. By the way, our first URL choice ( was already taken. Many kudos to my great committee as I did not have the talents to do this on my own. Our design and initial server stayed in place for many years.

          As chair of the Graphics Section, I initiated the student poster competition where elementary and high-school students pose a question of interest, collect and analyze the related data, and display the results in a poster. I got the idea by attending an ISI conference in Tokyo. Such a contest was in place in Japan, where over 10k students submitted entries. The winning posters were displayed at JSM. I arranged an opening reception and invited the ambassador from Japan to attend. We were honored by the vice ambassador. The most recent poster winners were featured in August 2016 Amstat News.

          The first ASA Data Expo presented a data set for members to analyze and display their results in a poster session at JSM. It was an artificial data set that, when projected the right way, displayed the word eureka. I decided that it would be a more meaningful exercise if we used real data. Thus, for the next several years, I chaired the Data Expo and obtained real data to be analyzed: crab fishery data from Alaska Fish and Wildlife, places rated data from the Places Rated Almanac, and baseball salary data. I even arranged for crab legs to be donated from Alaska and put on a crab dinner for those who participated. The Data Expo continued for 30 years, until 2013, and featured the analysis of real data.

          But, these are examples from the past. What would I like to do in the future?

          Increase public and business awareness of statistics. Anyone who has a computer with some number crunching software feels that he/she can analyze data properly. My daughter worked at a business intelligence company. When she tried to convince them to hire someone with training in statistics, they felt there was no need for that since she could fill that bill with her two statistics courses in college. Yet, their business was based on the analysis of Big Data sets and advising clients about important business decisions based on this analysis. This situation is all too common. We need to develop a program to educate businesses about the need for trained statisticians and the benefits they could reap by hiring them.

          Sustainability of our society. Of the 7,000 JSM participants last year, about 1,500 were students. But, do the students continue to join the ASA upon reaching the business world? For the most part, not a large enough percentage does. We need to develop programs that will interest students in continuing their membership and becoming active members of our society. We can run focus groups for students at JSM. Doing so will give us ideas to implement so the ASA will better meet their needs. I will also work with our chapters to encourage and support them in sponsoring local meetups, targeting JSM first-timers or potential members.

          Webinars are a popular vehicle these days for conducting meetings and training. We could add more of them to our offerings. Be assured that, if elected, I will initiate one or more programs that will have lasting value to our society and profession. I hope you will provide me that opportunity.

          Katherine Monti

          Retired from Rho, Inc.

          I am indeed honored to be considered to be a vice president of the ASA. The field of statistics has come a long way since I joined the ASA in 1975 (two years before John Tukey formally introduced box and whisker plots in his Exploratory Data Analysis). The association has come a long way, too. Neither statistics nor the ASA will stop changing, and that’s a good thing.

          Katherine Monti

          The ASA is always changing as the membership grows. But the association not only needs new statisticians, the world needs more statisticians. Encouraging students to become statisticians has been a continuing goal for many of us. To this end, I have enjoyed giving talks at career nights, hosting career-oriented roundtables, and contributing to the Amstat News career-oriented series (A Day in the Life of a Statistician and STATtr@k).

          The Biopharmaceutical Section’s pharmaceutical statisticians video has demonstrated that outreach efforts really work to attract young folks to the field. Reaching out works! But we have to keep finding new and engaging ways to encourage students to play in all corners of our diverse professional sandbox, or to at least let them know about statistics, because even those who choose other careers need to know at least some statistics!

          Encouraging the appropriate application of statistics is another crucial challenge. I have seen a legal case partially derailed by a PhD nonstatistician with his own way of thinking about data, an MD nonstatistician achieve significance in a clinical trial by treating the three-month data and the six-month data on the same patients as independent results, and, well, we all have our horror stories. Even daily news reports can give us pause: Did that study in the headlines control for the covariates that bias the results if not taken into account? All too frequently, the answer is no.

          As our Big Tent for Statistics grows even wider, we need to work on “Big Education” and “Big Communication” regarding the principles of design, the methods of analysis, and the ethical and valid interpretation of results. What types of programs increase our numbers and expand the appropriate use of statistics in applications?

          Many academic programs now incorporate supervised consulting experience into interdisciplinary consulting labs, some of which contribute to training of statisticians in developing countries. The ASA has backed pro bono efforts such as the student-run StatCom (Statistics in the Community) and the outreach group Statistics without Borders. The newly instituted ThisIsStatistics campaign uses social media to encourage the exploration of statistics, and expanding the use of podcasts is one of the strategic initiatives of 2017 ASA President Barry Nussbaum. All of these efforts demonstrate our commitment to encouraging interest in the field and in the sound use of statistics. Tweets, podcasts, Facebook, Pinterest, K–12 online resources—the association is evolving with the times and will necessarily continue to do so. The ASA has a lot to tackle as it continues to evolve, so a broad perspective is valuable.

          The job portion of my (very rewarding) career has taken me from academia to non-pharma industry to devices to pharmaceuticals (at a sponsor and then at a CRO), with some additional consulting along the way. The equally rewarding service portion of my career includes diverse leadership roles in ASA chapters, committees, sections, and the board. If elected, I look forward to bringing all these perspectives to serve the ASA as it moves forward. Member input is always highly valued, so please share your ideas with any of those serving on the ASA Board. Remember: Voting in the ASA election is an important form of input!

            Running for COSGB Representative to the Board
              John L. Czajka

              Senior Fellow, Mathematica Policy Research, Inc.

              I would be honored to serve as one of the Council of Section’s representatives to the board of directors. As a recent chair of the Council of Sections, an earlier vice chair, and a former officer in three sections, I believe that I am well prepared to represent the sections on the ASA Board of Directors.

              John L. Czajka

              Under the first of three themes, “Fulfilling Our Role as ‘The Big Tent for Statistics,’” the ASA’s Strategic Plan observes that a strength of the association is its mix of members from education, business and industry, and government. Students have provided the largest source of growth in ASA membership for a number of years, but retention of student members once they complete their degrees has been low. Retaining members who leave academia may pose the greatest challenge, but is critical to maintaining the diversity of our membership and achieving other goals of the association. One of my priorities as a member of the board of directors would be to expand the ASA’s efforts to retain those former student members who have begun careers in business and industry and government. This must go hand-in-hand with continuing to support our strong academic membership.

              Under the second theme of “Increasing the Visibility of the Profession,” I strongly support the ASA’s efforts to “promote the value of sound statistical practice” in policymaking. The importance of increasing the visibility of our association and profession will undoubtedly grow in the next few years. I commend the ASA for its initiatives in this area, which include the preparation of white papers, advocacy in support of the federal statistical system and major research budgets, the release of policy statements, and the development of resources for policymakers. As a participant in the public policy arena professionally, I would work as a board member to enhance these activities.

              As one who has been active in sections from early on, I am puzzled that fewer than half of the ASA’s members belong to sections. We need to understand why this is so and whether greater participation in sections is a goal that the ASA should pursue. For starters, I would work with the ASA to determine how we could enhance our membership data to enable us to better address fundamental questions about participation in sections. For example, how many of those who do not belong to sections once did so—and for how long? More effectively serving our membership may very well involve an expanded role for the sections.

              The ASA has been a keystone of my professional life for more than 30 years. I would welcome the opportunity to share my extensive experience by serving on the association’s board of directors.

              Katherine Halvorsen

              Professor of Mathematics and Statistics, Smith College

              My primary concern for our profession is that we continue to promote awareness of the essential importance of statistics in natural and social science research, as well as in the public sphere, including government, industry, and education. The ASA’s strategic plan addresses my concern through its emphasis on membership growth in both numbers and diversity and on education from kindergarten through 12th grade, through post-graduate continuing education, and through outreach to special groups such as journalists and Capitol Hill staff. I strongly support the ASA’s work on the undergraduate curriculum, the statistical education of teachers, and outreach to K–12 teachers through Meeting Within a Meeting.

              Katherine Halvorsen

              Having served as chair of the Council of Sections, I have worked with groups applying to become new sections, as well as with the established sections, all of whom are concerned about having opportunities for their members to present invited sessions, panels, posters, and short courses at JSM. The proliferation of new sections fits neatly into the ASA’s goal of being “The Big Tent for Statistics,” encouraging a broadening and diversification of membership, but becomes unwieldy when we have to find opportunities for these groups to present at JSM. We need to find additional opportunities for members to present their work and network with others who share their interests. This might come through new specialized conferences (such as the Conference on Statistical Practice or Women in Statistics and Data Science), journals, webinars, or even newsletters. I would like to work with the board of directors to address these issues.

              I would be honored to serve as one of the Council of Section’s representatives to the ASA Board of Directors. I have been a member of the association since the 1980s and have served as chair and vice-chair of both the Council of Sections and the Advisory Committee on Continuing Education. I currently serve on the Leadership Support Council.

                Running for COCGB Representative to the Board
                  Donsig Jang

                  Vice President and Director, Center for Excellence in Survey Research, NORC at the University of Chicago

                  If elected to serve as a Council of Chapters representative to the board, I will work with other board members to support ASA to have a strategic plan (Enhancing the Diversity and Breadth of Our Association) well implemented. I strongly believe that this data-driven world brings us statisticians an exciting opportunity to bring our value to help improve every part of our lives. But it won’t happen without efforts.

                  Donsig Jang

                  As an applied statistician working in an environment with subject-matter researchers who are often highly quantitative, I strongly feel that real value statisticians should be able to bring to is not just statistical method, but statistical lens to solve problems. It requires understanding of fields we are working on, communications skills to converse with clients and collaborators in the field, and proactive leaderships to work together with team members.

                  I often made a joke that statistical value for a given project is not defined by a statistician, but by a project director or subject-matter expert. It’s largely true to many statisticians almost everywhere. I hope that the ASA provides necessary supports to members to help them have a right mindset as a statistician.

                  Another area I would like to work with other board members in is to help broaden statistics to embrace machine learning and other computation disciplines. In this Big Data era, it is our obligation to have statistical principles continue to be relevant in extracting right information from messy data. It needs an effort to have statistical methods bridged with computer science perspectives. I hope that the ASA will become a professional home for data scientists in coming years.

                  Last, ASA members have become diversified in many different ways in recent years. But there are many professionals who were trained in statistics or similar quantitative disciplines, but are not ASA members. I will work with the ASA Board to have ASA outreach to them, particularly those who are in nonacademic fields.

                  In closing, it is an honor and privilege to get nominated as a candidate for COCGB. I will continue to support ASA strategic plan and serve for whatever capacity I am allowed, regardless of this election outcome.

                  Alexander Cambon

                  Mathematical Statistician, U.S. Food and Drug Administration

                  “A strength of the ASA is the mix of members from business/industry, government, and education …” (From Theme 1, ASA Strategic Plan). It has been my good fortune to work in all three of these categories due to my involvement in the ASA.

                  Alexander Cambon

                  In 1996, when I joined the ASA, I was a statistician teaching and implementing statistical process control, experiment design, and reliability testing in an industrial/manufacturing setting. ASA meetings and JSM increased my awareness of the growing field of biostatistics and clinical trials. I eventually became a biostatistician at the University of Louisville (U of L) Statistical Consulting Center. My connections in the ASA Kentucky Chapter played a vital role in facilitating this career opportunity. I went to meetings because I enjoyed the talks, and I enjoyed getting to know statisticians and their different areas of work. This type of informal setting can be an important part of networking and career building.

                  Many of us can probably think of ways ASA involvement has influenced/enhanced our careers. The membership fee is definitely a high-return investment. In telling our stories, let’s get the word out to “make the value of long-term membership evident to all groups that are well represented or ought to be well represented among ASA membership.” Strategies in the plan include expanding “our market research capabilities to provide more and better data about the needs and interests of members and potential members.”

                  Diversity has been a key part of my life. My father was an immigrant from Italy, where I lived when I was very young. After college, I served as a water resource and health development engineer in a small village in Burkina Faso, West Africa, for 2.5 years. Later, work took me overseas to teach short courses in reliability engineering. I then went to a Chinese school in Louisville to attain an intermediate speaking level in Chinese. The University of Louisville was a very diverse environment and I had many opportunities to practice Chinese on the bus or at work.

                  As part of my role as a biostatistician at U of L, I often organized local ASA chapter meetings, scheduled speakers, and attended JSM to present topics and attend ASA meetings as a chapter representative. In 2010, with help from members of the Cincinnati and Kentucky chapters, I organized a joint traveling course for the Kentucky and Cincinnati chapters. Joint meetings such as this were very popular and provided additional opportunities to connect. Afterward, I served as District 2 vice chair, Council of Chapters Governing Board. In this capacity, I endeavored to help the chapters that were more isolated by utilizing resources from ASA headquarters, as well as resources from/connections with other chapters.

                  The strength of local chapters and JSM also enhances the ASA’s ability to invest in and support another theme in the strategic plan: “Increasing the Visibility of Our Profession.” In today’s climate more than ever, our profession has vital input into increasingly complex areas of data science and analytics. Tools such as and are two of many ways the ASA is using to elevate public awareness.

                  I am honored to be a candidate for the COCGB representative to the ASA Board of Directors. If elected, I will be an advocate for local chapters through my membership in the Council of Chapters and the COCGB. I will work to see that appropriate parts of the strategic plan (examples are highlighted above) are implemented to “make the value of long-term membership evident to all groups that are well represented or ought to be well represented among ASA membership.”

                    Running for Publications Representative to the Board
                      Scott Evans

                      Senior Research Scientist, Center for Biostatistics in AIDS Research/Department of Biostatistics, Harvard University

                      It is an exciting and important time for statistics and the ASA. Rapidly evolving access to data and advances in science and technologies create many challenges. But these challenges are also unprecedented opportunities to advance science, education, and policy through discoveries that can change the world to better serve society.

                      Scott Evans

                      Statistics is a common denominator for much of science. We must strengthen our relationships and communications with data experts in other disciplines and the broader scientific community, media, and public. We must evolve with the data science and Big Data revolutions, promoting statistics at the core of these progressions.

                      The need for statistical expertise and leadership has never been greater. The ASA plays an indispensable leadership role as the preeminent professional association for statistics. The ASA’s Strategic Plan outlines three foundational themes. The first is enhancing ASA diversity and strength through membership, professional development, and publications. The ASA has more than 19,000 members with increasing student and senior memberships. Effort is needed to attract regular members.

                      ASA publications have prestigious worldwide reputations and are a major asset to the profession and the ASA. But publications face modern challenges: transition to electronic/open access introducing financial viability issues with reduced individual subscriptions; an irreproducibility pandemic where statistics is often the scapegoat; journal proliferation threatening quality and citation rates; and slow review processes. The ASA must proactively address these issues. It is a time of great change and promise for publications. The ASA can modernize processes to maintain publication quality, utility, and relevancy with continued transitioning to electronic/open distribution while responsibly addressing the implications. New publication/peer-review models (e.g., living/collaborative documents) that exploit technology to increase access and improve functionality (e.g., rapid reviews) are emerging and can be evaluated. The ASA and social media can engage members in the process. The ASA must seek balance, providing a vibrant journal portfolio that serves the diverse needs of ASA members while protecting against journal proliferation to ensure quality and impact. ASA publications also have the opportunity and responsibility to provide leadership and infrastructure for scientific issue positioning (e.g., ASA’s statement on statistical significance and p-values).

                      A second theme is ensuring the future of our profession through education, leadership development, and sound fiscal strategy. It is critical that the ASA help lead the transformation of statistics education and teaching in the K–12 and college levels to improve the statistical literacy of society. We must also improve training of our future generations of statisticians, focusing not only on fundamentals, but also on leadership, supporting intangible skills, and creative thinking (i.e., thinking first and then researching and executing). Learning statistics is one thing, but learning to be a statistician is another.

                      The final theme is increasing the visibility and appreciation of our profession through public awareness, impact on policy, and contributions to interdisciplinary collaborations. The statistical ambassadors program that trains statisticians to communicate with the media plays a crucial role. Expanding our role and impact in science policy is paramount. The ASA now provides leadership and an infrastructure for impacting areas such as climate change and forensics. While interaction with other disciplines is natural for statisticians, we must better communicate with collaborators, engaging as thought leaders in addition to technical roles. The perception of statisticians as calculators, service providers, and data warehouses must evolve to innovative strategists and problem solvers that turn information into knowledge to improve decision making.

                      Richard Levine

                      Professor of Statistics, San Diego State University Department of Mathematics and Statistics

                      Over the past 20 years, we have been confronted by a seemingly continuous attempt to rebrand our profession. The buzzwords of metrics (e.g., biometrics, chemometrics, and environmetrics), data mining, informatics, analytics, and now data science hit the scientific community, if not mainstream media, as we grapple with the deluge and complexity of data generated in this information age. The ASA leadership and board of directors have positioned our profession to be at the center of this movement. Recent developments that exemplify these directions include the ThisIsStatistics public relations campaign, data science–oriented curricula guidelines in K–12 and undergraduate statistics programs, PStat and GStat accreditations, and the p-value statement on good statistical practice. The board, and particularly new directors, must stay on top of, and more importantly ahead of, these data science trends.

                      Richard Levine

                      At the heart of the data science evolution are digital technologies that have and will provide awesome new opportunities for ASA publications. These challenges present themselves through dynamic scholarly communication systems: peer review models with quicker turnarounds; open access portals with article/blog feedback and review mechanisms; and reproducible research via seamless dissemination of data, code, and methods. The board will be challenged to meet the diverse statistical needs of our readership and the public broadly while maintaining our reputation for scientific excellence and publications of the highest quality.

                      I would be honored and excited to continue my service to the statistics profession as a member of the ASA Board of Directors. I believe my experience and expertise ideally situates me to represent our publications on the board and collaborate with our membership to shape our initiatives and place statistics as the leader of the data science crusade.

                        ASA Election Candidates List
                          COCGB (Council of Chapters Governing Board)

                          Isaac Nuamah
                          Johnson & Johnson Pharmaceutical R&D
                          Andrew Reilly

                          Vice-Chair, Region 1, District 1
                          Lynn Sleeper
                          Boston Children Hospital and Harvard Medical School
                          Ofer Harel
                          University of Connecticut

                          Vice-Chair, Region 1, District 2
                          Chandan Saha
                          Indiana University School of Medicine
                          David Fardo
                          University of Kentucky College of Public Health

                          COSGB (Council of Sections Governing Board)

                          Natalie Rotelli
                          Eli Lilly and Company
                          Marlene Egger
                          University of Utah Department of Family and Preventive Medicine

                          Stephine Keeton
                          Pharmaceutical Product Development, Inc.
                          Philip Scinto
                          The Lubrizol Corporation

                          Bayesian Statistical Sciences Section

                          Steven MacEachern
                          The Ohio State University
                          Susan Paddock
                          RAND Corporation

                          Program Chair-Elect
                          Robert B. Gramacy
                          Virginia Tech
                          Christopher Hans
                          The Ohio State University

                          Publication Officer
                          Anirban Battacharya
                          Texas A&M University
                          Xinyi Xu
                          The Ohio State University

                          Biometrics Section

                          Sheng Luo
                          The University of Texas Health Science Center at Houston
                          Candidate withdrew

                          Council of Sections Representative
                          Dipankar Bandyopadhyay
                          Virginia Commonwealth University
                          Jay Bartroff
                          University of Southern California

                          Biopharmaceutical Section

                          Xiaohui (Ed) Luo
                          PTC Therapeutics
                          Richard C. Zink
                          SAS Institute

                          Program Chair-Elect
                          Margaret Gamalo-Siebers
                          Eli Lilly and Company
                          Judy Li
                          U.S. Food and Drug Administration

                          Ugochi Emeribe
                          AstraZeneca Pharmaceuticals
                          Janelle K. Charles
                          U.S. Food and Drug Administration

                          Council of Sections Representative
                          Jennifer Gauvin
                          Novartis Pharmaceutical Corporation
                          Brian Millen
                          Eli Lilly and Company

                          Business and Economic Statistics Section

                          Peter Zadrozny
                          Bureau of Labor Statistics
                          Erika McEntarfer
                          U.S. Census Bureau

                          Program Chair-Elect
                          Marina Gindelsky
                          Bureau of Economic Analysis
                          Mariana Saenz
                          Georgia Southern University

                          Government Statistics Section

                          Michael Messner
                          Environmental Protection Agency
                          Elizabeth Mannshardt
                          U.S. Environmental Protection Agency

                          Program Chair-Elect
                          Jeffrey Gonzalez
                          Bureau of Labor Statistics
                          Jonathan Lyle Auerbach
                          Columbia University

                          Health Policy Statistics Section

                          Ofer Harel
                          University of Connecticut
                          Ruth Etzioni
                          Fred Hutchinson Cancer Research Center

                          Medical Devices and Diagnostics Section

                          Zhen Zhang
                          Abbott Vascular
                          Beimar Iriarte
                          Abbott Laboratories

                          Program Chair-Elect
                          Gerry Gray
                          Data-Fi, LLC
                          Martin Ho
                          Center for Devices and Radiological Health

                          Mental Health Statistics Section

                          Booil Jo
                          Stanford University School of Medicine
                          Satesh Iyengar
                          University of Pittsburgh

                          Program Chair-Elect
                          Dulal Bhaumik
                          University of Illinois at Chicago
                          Ramzi Nahhas
                          Wright State University

                          Nonparametric Statistics Section

                          Piotr Fryzlewicz
                          London School of Economics
                          Dimitris Politis
                          University of California, San Diego

                          Program Chair-Elect
                          Richard Samworth
                          University of Cambridge
                          Bing Li
                          Penn State University

                          Limin Peng
                          Emory University
                          Yoonkyung Lee
                          The Ohio State University

                          Publications Officer
                          Naveen Naidu Narisetty
                          University of Illinois at Urbana-Champaign
                          Po-Ling Loh
                          University of Wisconsin at Madison

                          Quality and Productivity Section

                          Brian P. Weaver
                          Los Alamos National Laboratory
                          Peng Liu
                          JMP Division, SAS Institute

                          Program Chair-Elect
                          Abdel-Salam Gomaa
                          Qatar University
                          Shan Ba
                          Procter & Gamble

                          Physical and Engineering Sciences Section

                          Byran J. Smucker
                          Miami University
                          Ananda Sen
                          University of Michigan

                          Program Chair-Elect
                          Xinwei Deng
                          Virginia Tech
                          Brad Evans
                          Pfizer, Inc.

                          Jennifer Kensler
                          Shell International Exploration and Production
                          Matthew T. Pratola
                          The Ohio State University

                          Risk Analysis Section

                          Jing Zhang
                          Miami University
                          Susan Simmons
                          North Carolina State University

                          Program Chair-Elect
                          Aric LaBarr
                          North Carolina State University
                          Jiwei Zhao
                          SUNY at Buffalo

                          Piaomu Liu
                          Bentley University
                          Christopher Sroka
                          New Mexico State University

                          Publications Officer
                          Lingling An
                          The University of Arizona
                          Maria Barouti
                          American University

                          Council of Sections Representative
                          Alexandra Kapatou
                          American University
                          Edsel Pena
                          University of South Carolina

                          Social Statistics Section

                          Tim Liao
                          University of Illinois at Urbana-Champaign
                          Trudi Renwick
                          U.S. Census Bureau
                          Eileen O’Brien
                          U.S. Energy Information Administration

                          Program Chair-Elect
                          Stephanie Ewert
                          U.S. Census Bureau
                          Candidate withdrew

                          Yulei He
                          National Center for Health Statistics
                          Stephanie Eckman
                          RTI International
                          Jiashen You
                          Department of Homeland Security and The George Washington University

                          Statistical Computing Section

                          Wendy Martinez
                          Bureau of Labor Statistics
                          David Hunter
                          Penn State University

                          Program Chair-Elect
                          Usha Govindarajulu
                          SUNY Downstate Medical Center
                          Sebastian Kurtek
                          The Ohio State University

                          Matthias Katzfuss
                          Texas A&M University
                          Jared Murray
                          Carnegie Mellon University

                          Council of Sections Representative
                          David van Dyk
                          Imperial College London
                          Rajib Paul
                          Western Michigan University

                          Statistical Consulting Section

                          Jonathan Mahnken
                          University of Kansas Medical Center
                          LeAnna Stork

                          Mekibib Altaye
                          Cincinnati Children’s Hospital
                          Chris Barker
                          Statistical Planning and Analysis Services, Inc.

                          Council of Sections Representative
                          Hsin-Yi (Cindy) Weng
                          University of Utah
                          Hrishikesh Chakraborty
                          University of South Carolina

                          Executive Committee at Large
                          Jason Machan
                          Lifespan Hospital System
                          Wei-Ting Hwang
                          University of Pennsylvania

                          Statistical Education Section

                          Mine Çetinkaya-Rundel
                          Duke University
                          Roger Woodard
                          North Carolina State University

                          Council of Sections Representative
                          Matt Hayat
                          Georgia State University
                          Adam Sullivan
                          Brown University

                          Executive Committee at Large
                          Leigh Johnson
                          Capital University
                          Sharon Lane-Getaz
                          St. Olaf College
                          Weiwen Miao
                          Haverford College
                          Cassandra Pattanayak
                          Wellesley College

                          Statistical Graphics Section

                          Dianne Cook
                          Monash University
                          Kaiser Fung
                          Columbia University Program
                          Mahbubul Majumder
                          University of Nebraska at Omaha

                          Program Chair-Elect
                          Edward Mulrow
                          NORC at the University of Chicago

                          Publications Officer
                          Joyce Robbins
                          Columbia University and NBR
                          Abbass Sharif
                          University of Southern California

                          Statistical Learning and Data Science Section

                          Heping Zhang
                          Yale University School of Public Health
                          Tian Zheng
                          Columbia University

                          Program Chair-Elect
                          Ali Shojaie
                          University of Washington
                          Vincent Vu
                          The Ohio State University

                          Statistical Programmers and Analysts Section

                          Candidate withdrew
                          Jonathan Lisic
                          National Agricultural Statistics Service

                          Program Chair-Elect
                          William Coar
                          Axio Research
                          Richard Schwinn
                          U.S. Small Business Administration

                          Marianne Miller
                          Eli Lilly and Company
                          Pratheepa Jeganathan
                          Stanford University

                          Amy Gillespie
                          Merck & Co., Inc.
                          Michael Yingling
                          Washington University School of Medicine

                          Publications Officer
                          Enayetur Raheem
                          Carolinas HealthCare System
                          Tasneem Zaihra
                          SUNY Brockport

                          Statistics and the Environment Section

                          Christopher Wikle
                          University of Missouri – Columbia
                          Jarrett Barber
                          Northern Arizona University

                          Program Chair-Elect
                          Will Kleiber
                          University of Colorado, Boulder
                          Alexandra Schmidt
                          McGill University

                          Maria Terres
                          The Climate Corporation
                          Ying Sun
                          King Abdullah University of Science and Technology

                          Publications Chair-Elect
                          Oksana Chkrebtii
                          The Ohio State University
                          K. Sham Bhat
                          Los Alamos National Laboratory

                          Council of Sections Representative
                          Jenise Swall
                          Virginia Commonwealth University
                          Wendy Meiring
                          University of California, Santa Barbara

                          Statistics in Defense and National Security Section

                          Jane Pinelis
                          The Johns Hopkins University
                          Taps Maiti
                          Michigan State University

                          Program Chair-Elect
                          Erin Hodgess
                          University of Houston
                          Kassandra Fronczyk
                          Institute for Defense Analyses

                          Statistics in Epidemiology Section

                          Jing Cheng
                          University of California, San Francisco
                          Kathleen Jablonski
                          The George Washington University

                          Program Chair-Elect
                          Yingqi Zhao
                          Fred Hutchinson Cancer Research Center
                          Veronica Berrocal
                          University of Michigan

                          Publications Officer
                          Yan Ma
                          The George Washington University
                          Colin Fogarty
                          MIT Sloan School of Management

                          Council of Sections Representative
                          Rebecca Yates Coley
                          Group Health Research Institute
                          Nandita Mitra
                          University of Pennsylvania

                          Statistics in Genomics and Genetics Section

                          Dan Nicolae
                          The University of Chicago
                          Dan Nettleton
                          Iowa State University

                          Program Chair-Elect
                          Hongkai Ji
                          The Johns Hopkins University
                          Li-Xuan Qin
                          Memorial Sloan Kettering Cancer Center

                          Council of Sections Representative
                          Pei Wang
                          Mount Sinai School of Medicine
                          Peng Wei
                          MD Anderson Cancer Center

                          Statistics in Imaging Section

                          Xi Luo
                          Brown University
                          Bin Nan
                          University of Michigan
                          Hernando Ombao
                          University of California at Irvine

                          Program Chair-Elect
                          Nicole Carlson
                          University of Colorado
                          Linglong Kong
                          University of Alberta
                          Ting-Ting Zhang
                          University of Virginia

                          Council of Sections Representative
                          Amanda Mejia
                          Indiana University
                          Dana Tudorascu
                          University of Pittsburgh

                          Statistics in Marketing Section

                          Lynd Bacon
                          Loma Buena Associates, Northwestern University, Notre Dame University
                          Victoria Gamerman
                          Boehringer-Ingelheim Pharmaceuticals, Inc.

                          Program Chair-Elect
                          Tim Trudell
                          Sarjinder Singh
                          Texas A&M University-Kingsville

                          Hiya Banerjee
                          Novartis Pharmaceuticals Inc.
                          Lihang Yin
                          Leaders Financial & Insurance Services, Inc.

                          Statistics in Sports Section

                          Luke Bornn
                          Simon Fraser University
                          Shane Reese
                          Brigham Young University

                          Program Chair-Elect
                          Sam Ventura
                          Carnegie Mellon University
                          Andrew Swift
                          University of Nebraska at Omaha

                          Council of Sections Representative
                          Stephanie Kovalchik
                          Tennis Australia/Victoria University
                          Kenny Shirley

                          Survey Research Methods Section

                          Kennon Copeland
                          NORC at the University of Chicago
                          Mansour Fahimi
                          GfK Custom Research

                          Program Chair-Elect
                          Asaph Young Chun
                          U.S. Census Bureau
                          Michael Sinclair
                          Mathematica Policy Research

                          Safaa Amer
                          RTI International
                          Bo Lu
                          The Ohio State University

                          Council of Sections Representative
                          Jamie Ridenhour
                          RTI International
                          Michael Yang
                          NORC at the University of Chicago

                          Teaching Statistics in the Health Sciences Section

                          Amy Nowacki
                          Cleveland Clinic
                          John McGready
                          Johns Hopkins Bloomberg School of Public Health

                          Member Spotlight: Mohammed Shayib

                          Wed, 03/01/2017 - 6:00am

                          I fled my home town—Alma, Safad, northern Palestine—with my family when I was only 5 years old.

                          This was during the first Arab-Israeli war, in 1948. I made the trip on a cow’s back to southern Lebanon.

                          For the next few years, my family moved from one village to another, seeking a place to make a living. It was then that I started my schooling.

                          It was my father who got me to love mathematics. He helped me memorize the multiplication tables, despite he and my mother never attending school. By the time I made it to sixth grade and was first in my class on the national exam, my family had settled in a village for Palestinian refugees.

                          It was in the village school. There were 30 students, from all grades, and we had one teacher. On my first day of school, I sat on a wooden step-stool, next to the teacher and near the door. Everyone started reading, one by one, and I could not take it. I ran next door to my parents. When my father saw me, he asked, “What are you doing here?” I said, “Everyone is reading except me.” He said I had to wait my turn to read and sent me back, instructing me to tell the teacher I had run to the restroom. I have been studying ever since.

                          It was my father who got me to love mathematics. He helped me memorize the multiplication tables, despite he and my mother never attending school. My half uncles did, so they helped me with reading and writing. By the time I made it to sixth grade and was first in my class on the national exam, my family had settled in a village for Palestinian refugees.

                          Throughout high school, I lived with my uncle and attended several schools in Sidon, Lebanon. During my last year, I went to the National Evangelical Institute, where I graduated among the top four in the class.

                          Thanks to a scholarship from the United Nationals Relief and Works Agency, I went to Ain Shams University in Cairo, Egypt, despite the six-days war in 1967 that interrupted the final exams. I earned my bachelor of science degree in special mathematics with first-class honors. After graduation, I became a full-time teaching assistant in the department of mathematics at the University of Riyadh, in Saudi Arabia. While there, I also earned my master’s degree at the University of Liverpool, UK, and AUB, Beirut, Lebanon.

                          I received my visa to go to the UK in 1970. On my way to buy my airplane ticket, a friend persuaded me to check with the American Friends of the Middle East State Department Agency in Beirut to see if I could get a scholarship to go to the United States. I did. They paid for my airplane ticket, my medical insurance, and my graduate degree for a year. I went on and finished my degree at AUB, Beirut, in August 1972. My thesis was titled “Number Theory: Gaussian Integers as Sums of Squares.” After I finished my master’s degree, I transferred to Texas Tech University and began work on my PhD in mathematics.

                          In early May of 1972, I met my future wife. It took a year to prepare for the wedding, but on August 18, 1974, she became my wife and joined me in El Paso, Texas. We have been married for almost 43 years now.

                          In the summer of 1976, I took a class in sampling theory. The course, instructed by Thomas Boullion, hooked me on statistics. The course was interesting; I like crunching numbers and making sense out of them. I wrote my dissertation on error rates in Poisson discrimination and graduated in 1979.

                          The week I earned my PhD, the chair of the department of mathematics at Texas Tech asked me if I was interested in working at Cottey College, a junior college for women in Nevada, Missouri. I said I was, but I had to go back to Maine and eventually back to Kuwait because of my visa. This was 1980.

                          By 1990, I had two daughters and two sons and had co-authored Applied Statistical Methods.

                          In August of 1990, Iraq invaded Kuwait. Because our oldest son was born in America, we were able to evacuate to the United States. We went back to Lubbock, Texas, where I taught for two years before joining Texas Instruments. During my tenure there, I was certified by the American Society for Quality as a Certified Quality Engineer. In 1998, I was laid off, but found a job at Texas Tech as a systems analyst until 2004, when I went to teach at Prairie View A&M University.

                          I retired this past August, but still love to teach statistics. Currently, I am an adjunct faculty member in the department of mathematics at Lone Star College.

                          I contribute to the ASA in memory of my parents, who were committed to me staying in school regardless of our resources. Moreover, it allows the ASA to promote awareness of numbers and data in general. When they are used correctly based on solid procedures, they can lead to a better life.

                          It’s What They Say They Heard

                          Wed, 03/01/2017 - 6:00am

                          ASA President Barry Nussbaum records episode 20, “A Statistician Clears the Air,” of the Stats + Stories podcast. (Courtesy of Stats + Stories)

                            Oh, how I vividly remember bumping into an assistant administrator of the Environmental Protection Agency in the elevator. He was a bit irked that he would be late for an all-day retreat of one of his major offices. Somewhat cynically, I suggested that if he arrived at the one-third point of the day and remarked, “Well, this all seems to boil down to a problem in communications,” he would probably be right on target. By the look on his face, he did not appear any less irked by my wisdom.

                            Several weeks later, when he happened to see me in the hallway, he told me that just before the lunch break at the retreat, one of his division directors stood up and opined that the problem seemed to be one of communications. And yes, this time he had a smile on his face.

                            I even noticed a Washington Post headline on December 27, 2016: Obama Blames Democrats’ November Defeat on Failure to Communicate Effectively. Why is it that lack of proper communications seems to hold up progress on all fronts and throughout all time?

                            I think the problem is particularly serious in our profession. In a data-driven analytic world, there seems to be more and more desire to present conclusions, suggestions, and recommendations based on statistical analysis. In the recent presidential election, we were pelted with survey results, each careful to mention the poll accuracy within plus or minus three percentage points.

                            Even in colloquial talk, one sees statistical intrusions. The weekend before the rather contentious presidential election, Peggy Noonan tried to calm the hassled electorate in a Wall Street Journal opinion column. She noted, “Someone is going to win Tuesday and then, if trendlines that have proved reliable in the past continue, the sun will come up on Wednesday.” She added humorously, “We claim this with a 3% margin of error.”

                            It may be counterintuitive, but I find the polling results accuracy statement sad and Noonan’s comment uplifting. Why? Because, in the polling, I would guess the 3% number is based on the sample size. What about nonsampling error—all the other things that may contribute to the error? Concerns such as randomness, representativeness, sampling frame, wording of questions, order of questions, and so forth would certainly increase the error range. So, I doubt the true accuracy, and hence the correct information from the survey, is being communicated properly. This is not even mentioning the communication problem in trying to explain how most polling efforts got the winner wrong!

                            And conversely, why do I like Noonan’s tongue-in-cheek remark? Because it shows she is cognizant that statistical reasoning must be employed, communicated, and reported accurately at some major points.

                            This creeps up again in the refrain familiar to every one of us who has ever introduced himself or herself as a statistician: “Oh, statistics. That was my worst course.” I used to be mildly amused by this truly predictable response. But it finally dawned on me that you rarely hear a similar remark concerning complex variables or atomic physics. I’m sure the public is not crazy about imaginary numbers, nor do they get the differences between fission, fusion, and confusion. So why does statistics take it on the chin?

                            Because of its importance to every facet of life, many more people are exposed to statistical principles. OK, they all may not like it, but at least they know life is subject to variability and uncertainty. Thus, we have an opportunity, and indeed an obligation, to properly communicate statistical concepts and the rudiments of statistical reasoning. And we must strive to do it so people understand the basic logic.

                            So why is all this of concern? Many of you have heard my mantra: “It’s not what we said, it’s not what they heard, it’s what they say they heard.” With our increased use of data, proper analysis is crucial. I certainly believe we have qualified statisticians who can do that. Then we must tell somebody what the analysis is all about—its aims, its methods, its shortcomings, its downsides. Again, we usually do this quite adequately. The next step is that the recipient of the information should hear what we are saying and hear it accurately. They may or may not ask pertinent questions. They may have other topics on their mind. Here, the statistician has an obligation to lend insight and try to ascertain if the message is getting through. This is the part I am not sure we always do well.

                            But it is the third element that is crucial: “It’s what they say they heard.” This is where the rubber hits the road. Somewhere, there is a policy maker, a decision maker, a judge, a jury, an elected official, a doctor who must properly integrate the results into real plans, real actions, judicial decisions, regulations, proper medications, and so forth.

                            This is a difficult task for statisticians. I have been there. As an expert statistical witness in a trial, you usually have just a few seconds to answer the loaded question of an adversarial attorney. You hope the judge or jury understands you and then, most importantly, they integrate it properly into their decision making.

                            I hesitate to add that this difficulty in communication might be exacerbated by our use of Twitter, Facebook, Instagram, etc., instead of full-fledged oral or written communication. Yes, I know this is old school. But I am concerned. While I am a true advocate of succinct explanations, I am not sure this can always be accomplished in 140 characters. Naturally, I am also concerned that we now seem to have a universe that allows alternative facts. If ever there were a time to describe our work effectively so as to integrate the true meaning into societal decisions, it is NOW.

                            I have given many talks in my career, and one of my main points has always been to encourage—even demand—that statisticians carefully review their raw data. I give examples of official data that are wrong. To lighten up a serious topic, I have for years shown a Dilbert cartoon in which Dilbert notes he didn’t have any accurate numbers so he just made up one. He further asserts that studies have shown that accurate numbers aren’t any more useful than the one you make up. Someone queries him as to how many studies have shown this and Dilbert answers, “87,” with absolute precision. Sadly, until this year, this was quite humorous.

                            So, what are we doing about all this? One of my initiatives is to make sure statistics are correctly giving the whole story. Here, the ASA is working with John Bailer and Richard Campbell at Miami University. John and his statistics colleagues have teamed up with Richard and his journalism counterparts to produce the series Stats + Stories. As John says, this is “the statistics behind the stories and the stories behind the statistics.” The idea, of course, is to tell the full story, accurately and forcefully, with the proper use of the statistical underpinnings.

                            I have had the pleasure of being interviewed for Stats + Stories in the context of environmental statistics. It was a terrific first-hand experience to learn the concerns and angles from both the statistical and journalistic sides of the table. To me, this goes a long way toward addressing the omnipresent communications problem.

                            Significantly forward,