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Censored Composite Conditional Quantile Screening for High-Dimensional Survival Data
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作者 LIU Wei LI Yingqiu 《应用概率统计》 CSCD 北大核心 2024年第5期783-799,共17页
In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all usef... In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated. 展开更多
关键词 high-dimensional survival data censored composite conditional quantile coefficient sure screening property rank consistency property
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Feature Screening for High-Dimensional Survival Data via Censored Quantile Correlation 被引量:1
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作者 XU Kai HUANG Xudong 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2021年第3期1207-1224,共18页
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 weightin... 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. 展开更多
关键词 Censored quantile correlation feature screening high-dimensional survival data rank consistency property sure screening property
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