False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation

• 成果类型: Article
• 署名作者: Du, Lilun; Guo, Xu; Sun, Wenguang; Zou, Changliang
• 署名单位: Hong Kong University of Science & Technology; Beijing Normal University; University of Southern California; Nankai University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1945459
• 发表日期: 2023
• 页码: 607-621
• 关键词: multiple; Robustness; PROPORTION; selection; accuracy; number; ORACLE; VALUES; tests

摘要

We develop a new class of distribution-free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening, and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data-driven threshold along the ranking to control the FDR. The SDA filter substantially outperforms the Knockoff method in power under moderate to strong dependence, and is more robust than existing methods based on asymptotic p-values. We first develop finite-sample theories to provide an upper bound for the actual FDR under general dependence, and then establish the asymptotic validity of SDA for both the FDR and false discovery proportion control under mild regularity conditions. The procedure is implemented in the R package sdafilter. Numerical results confirm the effectiveness and robustness of SDA in FDR control and show that it achieves substantial power gain over existing methods in many settings.