报告人：江源
报告地点：数学与统计学院415会议室
报告时间：2024年12月10日星期二10:00-11:00
报告摘要：
Multiple testing has been a prominent topic in statistical research. Despite extensive work in this area, controlling false discoveries remains a challenging task, especially when the test statistics exhibit dependence. Various methods have been proposed to estimate the false discovery proportion (FDP) under arbitrary dependencies among the test statistics. One key approach is to transform arbitrary dependence into weak dependence and subsequently establish the strong consistency of FDP and false discovery rate (FDR) under weak dependence. As a result, FDPs converge to the same asymptotic limit within the framework of weak dependence. However, we have observed that the asymptotic variance of FDP can be significantly influenced by the dependence structure of the test statistics, even when they exhibit only weak dependence. Quantifying this variability is of great practical importance, as it serves as an indicator of the quality of FDP estimation from the data. To the best of our knowledge, there is limited research on this aspect in the literature. In this paper, we aim to fill in this gap by quantifying the variation of FDP, assuming that the test statistics exhibit weak dependence and follow normal distributions. We begin by deriving the asymptotic expansion of the FDP and subsequently investigate how the asymptotic variance of the FDP is influenced by different dependence structures. Based on the insights gained from this study, we recommend that in multiple testing procedures utilizing FDP, reporting both the mean and variance estimates of FDP can provide a more comprehensive assessment of the study's outcomes.
主讲人简介：
Dr. Yuan Jiang is currently an Associate Professor and Co-Director of Graduate Studies in the Department of Statistics at Oregon State University. Dr. Jiang received his PhD in Statistics from UW-Madison in 2008 and had three years of postdoctoral training at Yale University. In 2011, he joined the Department of Statistics at Oregon State University as an Assistant Professor and was then promoted to Associate Professor in 2017. Dr. Jiang’s research interests include data integration, variable selection, multiple testing, network analysis, and statistical genetics. Dr. Jiang has published more than 40 papers on top-tier journals, including JASA, Biometrika, Biometrics, Journal of Computational and Graphical Statistics, Statistics in Medicine, etc.
