报告时间：2021年6月2日 下午14:00-15:00
报告地点：腾讯会议（会议ID：383 708 086）
报告嘉宾：郭旭
报告主题：A New Procedure for Controlling False Discovery Rate in Large-Scale t-tests
报告摘要
This paper is concerned with false discovery rate (FDR) 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.
报告人简介
郭旭博士，现为北京师范大学统计学院副教授，博士生导师。他于2014年获得香港浸会大学博士学位。郭旭自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。郭旭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。目前主持国家自然科学基金面上项目，主持完成国家自然科学基金数学天元基金和青年基金。