Adaptive Selection for False Discovery Rate Control Leveraging Symmetry

• 成果类型: Article
• 署名作者: Wang, Kehan; Chen, Yuexin; Han, Yixin; Xu, Wangli; Kong, Linglong
• 署名单位: Renmin University of China; Renmin University of China; University of Alberta
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2519814
• 发表日期: 2026-01-02
• 页码: 360-372
• 关键词: false discovery rate; feature selection; Multiple Testing; symmetry; empirical bayes analysis; confidence-intervals

摘要

Controlling the false discovery rate (FDR) in high-dimensional multiple testing has recently been advanced through mirror statistics via knockoff and data splitting. However, these approaches primarily emphasize the symmetry structure of the one-dimensional mirror statistics while inadvertently overlooking the distribution information from non-null features when determining the rejection region, potentially causing a power loss. To tackle this challenge, we present a novel framework termed symmetry-based adaptive selection (SAS), which leverages the symmetry property of the two-dimensional statistics associated with the null features to estimate the local FDR and thereby determine the rejection region. We provide theoretical evidence for the asymptotic validity of FDR control and emphasize the superior power performance of our proposed SAS. Extensive numerical results from both synthetic experiments and two real-world datasets demonstrate that the proposed SAS achieves satisfactory FDR control and significant power improvements over existing methods. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.