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A General Framework for Information Pooling in Two-Sample Multiple Testing

编辑:wfy 时间:2018年12月04日 访问次数:252

题目:A General Framework for Information Pooling in Two-Sample Multiple Testing

报告人: Wenguang Sun (University of Southern California)

时间:2018.12.12(周三)下午 2:00

地点:紫金港校区管理学院行政楼14楼1417报告厅

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.

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联系人:张立新 (stazlx@zju.edu.cn)