The Bahadur representation for kernel-type estimator of the quantile function under strong mixing and censored data

被引：4

作者：

Ajami, M. ^{[1
]}

Fakoor, V. ^{[1
]}

Jomhoori, S. ^{[2
]}

机构：

[1] Ferdowsi Univ Mashhad, Sch Math Sci, Dept Stat, Mashhad, Iran

[2] Univ Birjand, Fac Sci, Dept Stat, Birjand, Iran

来源：

STATISTICS & PROBABILITY LETTERS | 2011年 / 81卷 / 08期

关键词：

Censored dependent data; Kaplan-Meier estimator; Kiefer process; Law of the iterated logarithm; Strong Gaussian approximation; STRONG APPROXIMATION; TIME-SERIES; RANDOM-VARIABLES; IDENTIFICATION; SEQUENCES;

D O I：

10.1016/j.spl.2011.03.033

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 consider the kernel-type estimator of the quantile function based on the kernel smoother under a censored dependent model. The Bahadur-type representation of the kernel smooth estimator is established, and from the Bahadur representation we can show that this estimator is strongly consistent. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：1306 / 1310

页数：5

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