Model-Free Conditional Feature Screening with FDR Control

Zhaoxue Tong, Zhanrui Cai, Songshan Yang, Runze Li

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In this article, we propose a model-free conditional feature screening method with false discovery rate (FDR) control for ultra-high dimensional data. The proposed method is built upon a new measure of conditional independence. Thus, the new method does not require a specific functional form of the regression function and is robust to heavy-tailed responses and predictors. The variables to be conditional on are allowed to be multivariate. The proposed method enjoys sure screening and ranking consistency properties under mild regularity conditions. To control the FDR, we apply the Reflection via Data Splitting method and prove its theoretical guarantee using martingale theory and empirical process techniques. Simulated examples and real data analysis show that the proposed method performs very well compared with existing works. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)2575-2587
Number of pages13
JournalJournal of the American Statistical Association
Volume118
Issue number544
DOIs
StatePublished - 2023

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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