Predictor ranking and false discovery proportion control in high-dimensional regression

JOURNAL OF MULTIVARIATE ANALYSIS

卷:171

Article

Jeng, X. Jessie¹  Chen, Xiongzhi² 

[1] North Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA

[2] Washington State Univ, Dept Math & Stat, Pullman, WA 99164 USA

关键词: Multiple testing;    Penalized regression;    Sparsity;    Variable selection;   

DOI  :  10.1016/j.jmva.2018.12.006

来源: Elsevier

【 摘 要 】

We propose a ranking and selection procedure to prioritize relevant predictors and control false discovery proportion (FDP) in variable selection. Our procedure utilizes a new ranking method built upon the de-sparsified Lasso estimator. We show that the new ranking method achieves the optimal order of minimum non-zero effects in ranking relevant predictors ahead of irrelevant ones. Adopting the new ranking method, we develop a variable selection procedure to asymptotically control FDP at a user-specified level. We show that our procedure can consistently estimate the FDP of variable selection as long as the de-sparsified Lasso estimator is asymptotically normal. In simulations, our procedure compares favorably to existing methods in ranking efficiency and FOP control when the regression model is relatively sparse. Published by Elsevier Inc.

【 授权许可】

Free