Local linear quantile regression with truncated and dependent data

被引：2

作者：

Wang, Jiang-Feng ^{[1
]}

Ma, Wei-Min ^{[2
]}

Fan, Guo-Liang ^{[3
]}

Wen, Li-Min ^{[4
]}

机构：

[1] Zhejiang Gongshang Univ, Dept Stat, Hangzhou 310018, Peoples R China

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

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

[4] Anhui Polytech Univ, Sch Math & Phys, Wuhu 241000, Peoples R China

来源：

STATISTICS & PROBABILITY LETTERS | 2015年 / 96卷

基金：

中国国家自然科学基金;

关键词：

Asymptotic normality; Regression quantile; Local linear fitting; Truncated data; alpha-mixing; ASYMPTOTIC PROPERTIES; ESTIMATOR;

D O I：

10.1016/j.spl.2014.09.029

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 nonparametric regression quantile estimator by using the local linear fitting for left-truncated data, and establish the Bahadur-type representation and asymptotic normality of the proposed estimator when the observations form a stationary a-mixing sequence. Finite-sample performance of the estimator is investigated via simulation studies. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：232 / 240

页数：9

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