RAND Statistics Seminar Series

Selection and Estimation for Large-Scale Simultaneous Inference

Presented by Professor Brad Efron
Department of Statistics
Stanford University
October 28, 2004, 4:00 pm - reception at 3:30 p.m.
Santa Monica, CA

Abstract

Modern scientific technology is providing a new class of simultaneous inference problems for the applied statistician, where there are hundreds or thousands or even more hypothesis tests to consider at the same time. Microarrays epitomize this type of technology, but similar problems arise in proteomics, time of flight spectroscopy, flow cytometry, and functional Magnetic Resonance Imaging. I will consider two related problems: given a large number of simultaneous hypothesis testing problems, how can we Select the Non-Null cases; and how can we Estimate effect sizes for the Non-Nulls? The talk will use two microarray data sets to illustrate a simple theory that requires a minimum of mathematical modeling.