Center for Statistical Sciences Seminar
Abstract: With recent advances in technology, it has become increasingly common in practice to test a large number of hypotheses simultaneously. In this talk, I formulate the large-scale multiple testing problem in a compound decision theoretic framework and discuss oracle and asymptotically optimal data-driven procedures for false discovery rate (FDR) control. My presentation is divided into three parts: the first part develops oracle and adaptive compound decision rules for independent tests, the second part discusses simultaneous testing of grouped hypotheses, and the third part considers large-scale multiple testing under dependency. A key goal is to show that conventional FDR procedures, which are mostly p-value based, can be substantially improved by our new data-driven procedures that adaptively exploit the distributional, structural and external information of the sample. I also discuss results of simulations studies, as well as microarray data analyses from a human immunodeficiency study and a breast cancer study, for illustration of our methods and their comparison with alternative procedures. (Candidate for Assistant Professor in the Biostatistics Section of the Program in Public Health)
Brown Analysis Seminar
PDE Seminar
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