Model-Free Feature Screening and FDR Control With Knockoff Features

• 成果类型: Article
• 署名作者: Liu, Wanjun; Ke, Yuan; Liu, Jingyuan; Li, Runze
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University System of Georgia; University of Georgia; Xiamen University; Xiamen University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1783274
• 发表日期: 2022
• 页码: 428-443
• 关键词: Covariance Estimation; distance correlation; variable selection; filter

摘要

This article proposes a model-free and data-adaptive feature screening method for ultrahigh-dimensional data. The proposed method is based on the projection correlation which measures the dependence between two random vectors. This projection correlation based method does not require specifying a regression model, and applies to data in the presence of heavy tails and multivariate responses. It enjoys both sure screening and rank consistency properties under weak assumptions. A two-step approach, with the help of knockoff features, is advocated to specify the threshold for feature screening such that the false discovery rate (FDR) is controlled under a prespecified level. The proposed two-step approach enjoys both sure screening and FDR control simultaneously if the prespecified FDR level is greater or equal to 1/s, wheres is the number of active features. The superior empirical performance of the proposed method is illustrated by simulation examples and real data applications. Supplementary materials for this article are available online.