Weighted quantile regression for longitudinal data using empirical likelihood

被引：0

作者：

YUAN XiaoHui ^{[1
]}

LIN Nan ^{[2
]}

DONG XiaoGang ^{[1
]}

LIU TianQing ^{[3
]}

机构：

[1] School of Basic Science, Changchun University of Technology

[2] Department of Mathematics, Washington University in St.Louis

[3] School of Mathematics, Jilin University

来源：

Science China Mathematics | 2017年 / 60卷 / 01期

基金：

中国国家自然科学基金;

关键词：

empirical likelihood; estimating equation; influence function; longitudinal data; weighted quantile regression;

D O I：

暂无

中图分类号：

O212.1 [一般数理统计];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper proposes a new weighted quantile regression model for longitudinal data with weights chosen by empirical likelihood(EL). This approach efficiently incorporates the information from the conditional quantile restrictions to account for within-subject correlations. The resulted estimate is computationally simple and has good performance under modest or high within-subject correlation. The efficiency gain is quantified theoretically and illustrated via simulation and a real data application.

引用

页码：147 / 164

页数：18

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