Title : ( The Bahadur representation for kernel-type estimator of the quantile function under strong mixing and censored data )
Authors: masoud ajami , Vahid Fakoor , S. Jomhoori ,
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Abstract
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
Keywords
Censored dependent data Kaplan–Meier estimator Kiefer process Law of the iterated logarithm Strong Gaussian approximation
@article{paperid:1022029,
author = {Ajami, Masoud and Fakoor, Vahid and S. Jomhoori},
title = {The Bahadur representation for kernel-type estimator of the quantile function under strong mixing and censored data},
journal = {Statistics and Probability Letters},
year = {2011},
volume = {81},
number = {8},
month = {August},
issn = {0167-7152},
pages = {1306--1310},
numpages = {4},
keywords = {Censored dependent data
Kaplan–Meier estimator
Kiefer process
Law of the iterated logarithm
Strong Gaussian approximation},
}
%0 Journal Article
%T The Bahadur representation for kernel-type estimator of the quantile function under strong mixing and censored data
%A Ajami, Masoud
%A Fakoor, Vahid
%A S. Jomhoori
%J Statistics and Probability Letters
%@ 0167-7152
%D 2011
