Model-free conditional feature screening for ultra-high dimensional right censored data

ABSTRACT

This paper is concerned with the conditional feature screening for ultra-high dimensional right censored data with some previously identified important predictors. A new model-free conditional feature screening approach, conditional correlation rank sure independence screening, has been proposed and investigated theoretically. The suggested conditional screening procedure has several desirable merits. First, it is model free, and thus robust to model misspecification. Second, it has the advantage of robustness of heavy-tailed distributions of the response and the presence of potential outliers in response. Third, it is naturally applicable to complete data when there is no censoring. Through simulation studies, we demonstrate that the proposed approach outperforms the CoxCS of Hong et al. under some circumstances. A real dataset is used to illustrate the usefulness of the proposed conditional screening method.

KEYWORDS:

• Conditional feature screening
• model-free
• ranking consistency property
• sure screening property
• ultra-high dimensional right censored data

Acknowledgments

We really appreciate that Prof. Yi Li and Dr. Hyokyoung G. Hong were able to share with us gene names for the DLBCL dataset.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

Chen's research was supported by the National Natural Science Foundation of China (11501573 and 11771250) and National Social Science Foundation of China (17BTJ019).