Variable screening for ultrahigh dimensional censored quantile regression

ABSTRACT

Quantile regression is a flexible approach to assessing covariate effects on failure time, which has attracted considerable interest in survival analysis. When the dimension of covariates is much larger than the sample size, feature screening and variable selection become extremely important and indispensable. In this article, we introduce a new feature screening method for ultrahigh dimensional censored quantile regression. The proposed method can work for a general class of survival models, allow for heterogeneity of data and enjoy desirable properties including the sure screening property and the ranking consistency property. Moreover, an iterative version of screening algorithm has also been proposed to accommodate more complex situations. Monte Carlo simulation studies are designed to evaluate the finite sample performance under different model settings. We also illustrate the proposed methods through an empirical analysis.

KEYWORDS:

• Censored data
• quantile regression
• sure screening property
• ultrahigh dimensionality

Acknowledgements

The first two author contribute equally to this work. The authors thank the editors, the associate editor, and the reviewers for their comments that helped improve this paper significantly.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Zhou's work was supported by the State Key Program of National Natural Science Foundation of China (71331006), the State Key Program in the Major Research Plan of National Natural Science Foundation of China (91546202).