False Discovery Rate Control via Data Splitting

• 成果类型: Article
• 署名作者: Dai, Chenguang; Lin, Buyu; Xing, Xin; Liu, Jun S.
• 署名单位: Harvard University; Virginia Polytechnic Institute & State University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2022.2060113
• 发表日期: 2023
• 页码: 2503-2520
• 关键词: Post-selection Inference; confidence-intervals; variable selection; P-values; knockoffs

摘要

Selecting relevant features associated with a given response variable is an important problem in many scientific fields. Quantifying quality and uncertainty of a selection result via false discovery rate (FDR) control has been of recent interest. This article introduces a data-splitting method (referred to as DS) to asymptotically control the FDR while maintaining a high power. For each feature, DS constructs a test statistic by estimating two independent regression coefficients via data splitting. FDR control is achieved by taking advantage of the statistic's property that, for any null feature, its sampling distribution is symmetric about zero; whereas for a relevant feature, its sampling distribution has a positive mean. Furthermore, a Multiple Data Splitting (MDS) method is proposed to stabilize the selection result and boost the power. Surprisingly, with the FDR under control, MDS not only helps overcome the power loss caused by data splitting, but also results in a lower variance of the false discovery proportion (FDP) compared with all other methods in consideration. Extensive simulation studies and a real-data application show that the proposed methods are robust to the unknown distribution of features, easy to implement and computationally efficient, and are often the most powerful ones among competitors especially when the signals are weak and correlations or partial correlations among features are high. Supplementary materials for this article are available online.