Asymptotic normality for a non parametric estimator of conditional quantile with left-truncated data

被引：0

作者：

Yao, Mei ^{[1
,2
]}

Wang, Jiang-Feng ^{[3
]}

Lin, Lu ^{[1
]}

机构：

[1] Shandong Univ, Qilu Secur Inst Financial Studies, Jinan, Peoples R China

[2] Hefei Univ Technol, Sch Math, Hefei, Peoples R China

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

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2017年 / 46卷 / 13期

基金：

中国国家自然科学基金;

关键词：

Asymptotic normality; conditional quantile; double-kernel estimator; local linear fitting; truncated data; CENSORED-DATA; NONPARAMETRIC-ESTIMATION; TIME-SERIES; REGRESSION; MODEL;

D O I：

10.1080/03610926.2015.1124120

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 non parametric estimator of conditional distribution function by the double-kernel local linear approach for left-truncated data, from which we derive the weighted double-kernel local linear estimator of conditional quantile. The asymptotic normality of the proposed estimators is also established. Finite-sample performance of the estimator is investigated via simulation.

引用

页码：6280 / 6292

页数：13

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