Robust model-free feature screening for ultrahigh dimensional surrogate data

ABSTRACT

This paper is concerned with the feature screening for the ultrahigh dimensional data with covariates missing at random, and some surrogate variables are available. We propose a marginal screening procedure based on the augmented inverse probability weighted methods and the nonparametric imputation technique. Our proposed screening method utilizes the surrogate information efficiently to overcome the missing data problem. It is model free and possesses the sure screening property under some regular conditions. Monte Carlo simulation studies and a real data application are conducted to examine the performance of the proposed procedure.

KEYWORDS:

• Ultrahigh dimensional data
• missing at random
• feature screening
• sure screening property

Acknowledgments

We thank the associate editor and reviewers for their careful review and insightful comments, which have led to a significant improvement of this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by National Natural Science Foundation of China (11971404, 11771215), Natural Science Foundation of Jiangsu Province (BK20161530), Qing Lan Project of Jiangsu Province (2016), Humanity and Social Science Youth Foundation of Ministry of Education of China (19YJC910010) and Fundamental Research Funds for the Central Universities (20720171064, 20720181003).