An empirical likelihood approach to quantile regression with auxiliary information

被引：14
|
作者：
Tang, Cheng Yong [1 ]
Leng, Chenlei [1 ]
机构：
[1] Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore 117548, Singapore
来源：
STATISTICS & PROBABILITY LETTERS | 2012年 / 82卷 / 01期
关键词：
Auxiliary information; Empirical likelihood; Estimating equations; Quantile regression; CONFIDENCE-INTERVALS; LINEAR-REGRESSION; INFERENCE; ESTIMATORS; MODELS;
D O I：
10.1016/j.spl.2011.09.003
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：
We consider how to incorporate auxiliary information to improve quantile regression via empirical likelihood. We propose a novel framework and show that our approach yields more efficient estimates compared to those from the conventional quantile regression. The efficiency gain is quantified theoretically and demonstrated empirically via simulation studies. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码：29 / 36
页数：8
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