Asymptotic normality for a local composite quantile regression estimator of regression function with truncated data

被引：2

作者：

Wang, Jiang-Feng ^{[1
]}

Ma, Wei-Min ^{[2
]}

Zhang, Hui-Zeng ^{[3
]}

Wen, Li-Min ^{[4
]}

机构：

[1] Zhejiang Gongshang Univ, Dept Stat, Hangzhou 310036, Peoples R China

[2] Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China

[3] Hangzhou Normal Univ, Sch Sci, Hangzhou 310036, Zhejiang, Peoples R China

[4] Jiangxi Normal Univ, Dept Stat, Nanchang 330022, Jiangxi, Peoples R China

来源：

STATISTICS & PROBABILITY LETTERS | 2013年 / 83卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Composite quantile regression; Local linear regression; Truncated data; Asymptotic normality; VARIABLE SELECTION; EFFICIENT;

D O I：

10.1016/j.spl.2013.02.022

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 construct a local linear composite quantile regression (CQR) estimator of regression function for left-truncated data, which extends the CQR method to the left-truncated model. The asymptotic normality of the proposed estimator is also established. The estimator is much more efficient than the local linear regression estimator for commonly-used non-normal error distributions via simulations. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.

引用

页码：1571 / 1579

页数：9

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