Title（问题）：Large-scale multiple testing under dependence

Speaker（报告人）：Assistant Professor Wenguang Sun

North Carolina State University, USA

Time（时间）：2011年1月5日（周三）下昼3:00-4:00

Place（所在）：北京大学理科一号楼1303课堂

Abstract（摘要）：This talk considers the problem of multiple testing under dependence in a compound decision theoretic framework. The observed data are assumed to be generated from an underlying two-state hidden Markov model. We propose oracle and asymptotically optimal data-driven procedures that aim to minimize the false non-discovery rate (FNR) subject to a constraint on the false discovery rate (FDR). It is shown that the performance of a multiple testing procedure can be substantially improved by adaptively exploiting the dependence structure among hypotheses, and hence conventional FDR procedures that ignore this structural information are inefficient. Both theoretical properties and numerical performances of the procedures proposed are investigated. It is shown that the procedures proposed control FDR at the desired level, enjoy certain optimality properties and are especially powerful in identifying clustered non-null cases. Extensions for set-wise multiple testing and pattern identification, as well as applications in genome-wide association studies and microarray time course experiments, are also discussed.

About the Speaker（报告人简介）：Dr. Wenguang Sun got his B. S. degree in Statistics from Peking University (2003) and Ph. D degree in Biostatistics (2008) from University of Pennsylvania (Advisor: Professor T. Tony Cai from Wharton School). Now he is an assistant professor at the Statistics department of North Carolina State University.