Weighted composite quantile regression with censoring indicators missing at random

Abstract

In this paper, we consider the weighted composite quantile regression for the linear model when the data are right censored and the censoring indicators are missing at random. The adaptive penalized procedures are proposed to discuss variable selection in the model. Under appropriate assumptions, the asymptotic normality and oracle property of these estimators are also established. The simulation studies are conducted to illustrate the finite sample performance of the proposed methods.