报告简介Abstract

This paper is concerned with false discovery rate ( control in large scale multiple testing problems We first propose a new data driven testing procedure for controlling the FDR in large scale t tests for one sample mean problem The proposed procedure achieves exact FDR control in finite sample settings when the populations are symmetric no matter the number of tests or sample sizes Comparing with the existing bootstrap method for FDR control, the proposed procedure is computationally efficient We show that the proposed method can control the FDR asymptotically for asymmetric populations even when the test statistics are not independent We further show that the proposed procedure with a simple correction is as accurate as the bootstrap method to the second order degree, and could be much more effective than the existing normal calibration We extend the proposed procedure to two sample mean problem Empirical results show that the proposed procedures have better FDR control than existing ones when the proportion of true alternative hypotheses is not too low, while maintaining reasonably good detection ability

嘉宾简介 About the Speaker

郭旭博士,现任北京师范大学统计学院副教授博士生导师。郭旭副教授于 2014 年获得香港浸会大学博士学位。郭旭副教授自2018 年 9 月至 2020 年 2 月作为助理研究教授 (Assistant Research Professor) 访问美国宾州州立大学统计系。旭副教授一直 从事模型设定检验 、 高维数据分析和半参数回归分析等方面的研究 并取得了一系列的研究成果。

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