Conditional distance correlation sure independence screening for ultra-high dimensional survival data

Abstract

In this article, we propose a new conditional feature screening procedure for ultra-high dimensional survival data via the conditional distance correlation. Compared with the existing methods, the proposed conditional feature screening approach has two key advantages. First, it is model-free and thus robust to model misspecification. Second, it is robust to heavy tails or extreme values in both of the covaraites and response. The sure screening property of suggested means is well established under rather mild assumptions. Simulation studies are carried out to examine and compare the performance of the advised procedure with its competitors, while a real data example is analyzed to illustrate the proposed approach.

Keywords:

• Conditional distance correlation
• conditional feature screening
• sure screening property
• survival data
• ultra-high dimensional data

Acknowledgments

The authors want to thank the editor, associate editor and referees for their valuable and constructive comments, which improve the quality of this paper.

Additional information

Funding

Wang and Chen’s research was supported by National Social Science Foundation of China (17BTJ019).