Good words from the NY Times:
"For Today's Graduate, Just One Word: Statistics" here .
"Advertising Companies Fret Over a Digital Talent Gap" here .
"What Are the odds That Stats Would Be This Popular?" here .
"What You (Really) Need to Know" here .
And from Science
"Why Statistics" here
"Statistical Insights are Crucial" here .
Bin Yu on Data Science here .
Statistics 840 Statistical Model Building and Learning will be given in the fall. A course description is here. Stat 709 NOT required.
Link to Conference on Nonparametric Statistics for Big Data here
IJ Schoenberg-Hilldale Professor of Statistics,
Professor of Biostatistics and Medical Informatics,
and Professor of Computer Sciences (by courtesy).
Department of Statistics
University of Wisconsin-Madison
Medical Science Center
1300 University Ave
Madison, WI 53706 USA
Fax: (608) 262-0032
e-mail wahba "at" stat.wisc.edu (replace "at" with the "at" symbol).
Coded email from people I dont know may not be read, plaintext please.
Wisconsin Senior Olympics 2013 medalist smiles summer winter photos David Callan
TR LIST: Click here for Tech Reports and mss
MINISEM ANNOUNCEMENTS The occasional Miniseminar informal brown bag talks are announced here.
SOFTWARE: Links to **LPS Algorithm****new**, RKPACK, GRKPACK, GCVPACK, NETLIB/GCV, R.
TALKS: Directory for talk abstracts, references and some overheads.
BOOK: "Spline Models for Observational Data" is in the SIAM e-books library. If you have a University of Wisconsin-Madison ID you can find it here
PhD STUDENTS Prospective, In Progress and Former PhD students
PUBLICATIONS PubList, SearchLinks to MathRev and Zentralblatt ..
Keywords for research: risk factor estimation; variational data assimilation; climate data analysis; demographic data analysis; ill-posed inverse problems; variational methods; adaptive tuning; smoothing spline ANOVA; bias-variance tradeoff; generalized cross-validation; reproducing kernel Hilbert space methods; supervised machine learning; radial basis functions; support vector machines; numerical methods for large data sets; dissimilarity information.