Censored Composite Conditional Quantile Screening for High-Dimensional Survival Data

高维生存数据的删失复合条件分位数筛选

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.

本文提出了一种删失复合条件分位数系数(cCCQC),用于评估高维删失回归模型中各预测变量的相对重要性.cCCQC利用了跨分位数的所有有用信息,能够有效地检测非线性效应,包括交互作用和异质性.此外,基于cCCQC的筛选方法对异常值具有鲁棒性,并具有确定筛选性质.模拟结果表明,该方法在高维预测变量的生存数据集中表现良好,尤其是在变量高度相关的情况下.