Spearman Rank Correlation Screening for Ultrahigh-Dimensional Censored Data

Herein, we propose a Spearman rank correlation-based screening procedure for ultrahigh-dimensional data with censored response cases. The proposed method is model-free without specifying any regression forms of predictors or response variables and is robust under the unknown monotone transformations of these response variable and predictors. The sure-screening and rank-consistency properties are established under some mild regularity conditions. Simulation studies demonstrate that the new screening method performs well in the presence of a heavy-tailed distribution, strongly dependent predictors or outliers, and offers superior performance over the existing nonparametric screening procedures. In particular, the new screening method still works well when a response variable is observed under a high censoring rate. An illustrative example is provided.

Authors

• Hongni Wang Shandong University of Finance and Economics
• Jingxin Yan Academy of Mathematics and Systems Science, Chinese Academy of Sciences
• Xiaodong Yan Zhongtai Securities Institute for Financial Studies, Shandong University

Keywords

• ML: Classification and Regression
• ML: Dimensionality Reduction/Feature Selection