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

XU Kai · HUANG Xudong

Journal of Systems Science & Complexity ›› 2021, Vol. 34 ›› Issue (3) : 1207-1224.

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Journal of Systems Science & Complexity ›› 2021, Vol. 34 ›› Issue (3) : 1207-1224. DOI: 10.1007/s11424-020-9295-5

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

  • XU Kai · HUANG Xudong
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Abstract

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.

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XU Kai · HUANG Xudong. Feature Screening for High-Dimensional Survival Data via Censored Quantile Correlation. Journal of Systems Science and Complexity, 2021, 34(3): 1207-1224 https://doi.org/10.1007/s11424-020-9295-5
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