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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This work is supported by the National Natural Science Foundation of China under Grant No. 11901006 and the Natural Science Foundation of Anhui Province under Grant Nos. 1908085QA06 and 1908085MA20.
This paper was recommended for publication by Editor ZHU Liping.
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Xu, K., Huang, X. Feature Screening for High-Dimensional Survival Data via Censored Quantile Correlation. J Syst Sci Complex 34, 1207–1224 (2021). https://doi.org/10.1007/s11424-020-9295-5
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DOI: https://doi.org/10.1007/s11424-020-9295-5