Censored Composite Conditional Quantile Screening for High-Dimensional Survival Data

In this paper we introduce the censored composite conditional quantile coeffcient (cCCQC for short) 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. Further, the proposed screening method based on cCCQC is robust against 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, especially when the variables are highly correlated.