RAND Statistics Seminar Series
Data Large-Scale Prediction Problems
Presented by Professor Bradley Efron, Stanford University
Thursday, May 21, 2009
10:30 a.m. – 12:00 p.m. PT / 1:30pm – 3:00pm ET
Conference Room 1226 and 1228
RAND Corporation, Santa Monica, CA
Please contact Denise Miller if you would like to attend this seminar.
Classical prediction methods such as Fisher's linear discriminant function were designed for small-scale problems, where the number N of candidate predictors was much smaller than the number of observations n. Modern scientific devices often reverse this situation. A microarray analysis, for example, might include n=100 subjects measured on N=10,000 genes, each of which is a potential predictor. I will discuss "Ebay", an empirical Bayes prediction algorithm designed to handle N >> n situations. It is closely related to the Shrunken Centroids algorithm of Tibshirani, Hastie, Narasimhan, and Chu.
Dr. Efron, the Max H. Stein Professor and Professor of Statistics and of Health Research and Policy, recently received the National Medal of Science and was cited "for his contributions to theoretical and applied statistics, especially the bootstrap sampling technique; for his extraordinary geometric insight into nonlinear statistical problems; and for applications in medicine, physics and astronomy." He previously had been awarded the Ford Prize, MacArthur Prize, and the Wilks Medal and has served as president of the American Statistical Association and of the Institute of Mathematical Statistics. Dr. Efron's work has spanned both theoretical and applied topics, including empirical Bayes analysis, applications of differential geometry to statistical inference, the analysis of survival data, and inference for microarray gene expression data.
Attending a Seminar
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