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.
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.