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.
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页码：1306 / 1310
页数：5
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