Previous Articles    

Feature Screening for High-Dimensional Survival Data via Censored Quantile Correlation

XU Kai · HUANG Xudong   

  1. XU Kai (Corresponding author) · HUANG Xudong
    School of Mathematics and Statistics, Anhui Normal University, Wuhu 241002, China.
    Email: tjxxukai@ahnu.edu.cn; huangxdahnu@163.com.
  • Online:2021-06-25 Published:2021-03-11

XU Kai · HUANG Xudong. Feature Screening for High-Dimensional Survival Data via Censored Quantile Correlation[J]. Journal of Systems Science and Complexity, 2021, 34(3): 1207-1224.

This paper proposes a new sure independence screening procedure for high-dimensional survival data based on censored quantile correlation (CQC). This framework has two distinctive features: 1) Via incorporating a weighting scheme, our metric is a natural extension of quantile correlation (QC), considered by Li (2015), to handle high-dimensional survival data; 2) The proposed method not only is robust against outliers, but also can discover the nonlinear relationship between independent variables and censored dependent variable. Additionally, the proposed method enjoys the sure screening property under certain technical conditions. Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors.

No related articles found!
Viewed
Full text


Abstract