Variable selection and coefficient estimation via composite quantile regression with randomly censored data

被引：24

作者：

Jiang, Rong ^{[1
]}

Qian, Weimin ^{[1
]}

Zhou, Zhangong ^{[2
]}

机构：

[1] Tongji Univ, Dept Math, Shanghai 200092, Peoples R China

[2] Jiaxing Univ, Dept Stat, Jiaxing 314001, Peoples R China

来源：

STATISTICS & PROBABILITY LETTERS | 2012年 / 82卷 / 02期

关键词：

Kaplan-Meier estimator; Randomly censored data; Composite quantile regression; Variable selection; LASSO; SURVIVAL ANALYSIS; ADAPTIVE LASSO; SHRINKAGE; EFFICIENT;

D O I：

10.1016/j.spl.2011.10.017

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：

Composite quantile regression with randomly censored data is studied. Moreover, adaptive LASSO methods for composite quantile regression with randomly censored data are proposed. The consistency, asymptotic normality and oracle property of the proposed estimators are established. The proposals are illustrated via simulation studies and the Australian AIDS dataset. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：308 / 317

页数：10

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