Model free feature screening for ultrahigh dimensional covariates with right censored outcomes

Abstract

This paper is concerned with feature screening for the ultrahigh dimensional survival data. We propose a new feature screening procedure by extending the method of Zhu et al. via inverse probability censoring weighting. The proposed procedure enjoys two appealing merits. First, it does not need to specify any model assumption between the response and the covariates. Thus, it is robust to the model mis-specification. Second, our procedure is robust in the presence of outliers or extreme values since it only uses the rank of censored outcomes. We establish the sure screening property under some regular conditions. The simulations and analysis of the real data demonstrate that our procedure exhibits favorably in comparison with the existing competitors.