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  • 统计与管理学院2018年学术报告第48期

    【主 题】 A General Framework for Information Pooling in Two-Sample Multiple Testing

    【报告人】 Wenguang Sun, 副教授

               University of Southern California

    【时 间】 2018年12月18日(星期二)14:00-15:00

    【地 点】 上海财经大学统计与管理学院大楼1312会议室

    摘 要】In this talk, we discuss a general framework for exploiting the sparsity information in two-sample multiple testing problems. We propose to first construct a covariate sequence, in addition to the usual primary test statistics, to capture the sparsity structure, and then incorporate the auxiliary covariates in inference via a three-step algorithm consisting of grouping, adjusting and pooling (GAP). The GAP procedure provides a simple and effective framework for information pooling. An important advantage of GAP is its capability of handling various dependence structures such as those arise from multiple testing for high-dimensional linear regression, differential correlation analysis, and differential network analysis. We establish general conditions under which GAP is asymptotically valid for false discovery rate control, and show that these conditions are fulfilled in a range of applications. Numerical results demonstrate that existing methods can be significantly improved by the proposed framework. This is the joint work with Yin Xia from Fudan University and Tony Cai from University of Pennsylvania.

    嘉宾简介】Wenguang Sun is an associate professor in the department of data science and operations at the Marshall School of Business, University of Southern California. His research interests include large-scale multiple testing, selective inference and statistical decision theory.