RAND Statistics Seminar Series
Simultaneous Inference: When Should Hypothesis Testing Problems Be Combined?
Presented by Bradley Efron, Stanford University
Thursday November 29, 2007
1:00 P.M. PDT
Conference Room 3312
RAND Corporation, Santa Monica, CA
Please contact Denise Miller if you would like to attend this seminar.
Statisticians these days are often presented with hundreds or thousands of hypothesis testing problems to evaluate at the same time -- generated from modern scientific technologies such as microarrays, fMRI scanners, imaging satellites, and flow cytometry counters. The relevant statistical literature usually begins with the tacit assumption that a single combined analysis, for instance a False Discovery Rate assessment, should be applied to the entire set of problems at hand. I will give examples to show the dangers of this assumption. A little bit of theory on when to separate and when to combine will be presented, but the examples make clear that actual situations can be dauntingly difficult to decide.
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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