Weighted composite quantile regression with censoring indicators missing at random

被引：5
|
作者：
Wang, Jiang-Feng [1 ]
Jiang, Wei-Jun [1 ]
Xu, Fang-Yin [1 ]
Fu, Wu-Xin [1 ]
机构：
[1] Zhejiang Gongshang Univ, Dept Stat, Hangzhou 310018, Zhejiang, Peoples R China
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2021年 / 50卷 / 12期
关键词：
Composite quantile regression; variable selection; missing at random; asymptotic normality; censoring indicator; ADDITIVE HAZARDS REGRESSION; PRODUCT-LIMIT ESTIMATORS; MODEL;
D O I：
10.1080/03610926.2019.1678638
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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.
引用
收藏
页码：2900 / 2917
页数：18
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