Feature screening in ultrahigh-dimensional additive Cox model |
| |
Authors: | Guangren Yang Sumin Hou Luheng Wang Yanqing Sun |
| |
Institution: | 1. Department of Statistics, School of Economics, Jinan University, Guangzhou, People's Republic of China;2. School of Statistics, Beijing Normal University, Beijing, People's Republic of China;3. Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC, USA |
| |
Abstract: | The additive Cox model is flexible and powerful for modelling the dynamic changes of regression coefficients in the survival analysis. This paper is concerned with feature screening for the additive Cox model with ultrahigh-dimensional covariates. The proposed screening procedure can effectively identify active predictors. That is, with probability tending to one, the selected variable set includes the actual active predictors. In order to carry out the proposed procedure, we propose an effective algorithm and establish the ascent property of the proposed algorithm. We further prove that the proposed procedure possesses the sure screening property. Furthermore, we examine the finite sample performance of the proposed procedure via Monte Carlo simulations, and illustrate the proposed procedure by a real data example. |
| |
Keywords: | The additive Cox model partial likelihood spline approximations ultrahigh-dimensional survival data |
|
|