Title : ( Central Limit Theorem for ISE of Kernel Density Estimators in Censored Dependent Model )
Authors: Sara Jomhoori , Vahid Fakoor , Hassan Ali Azarnoosh ,
Full TextAbstract
In some long-term studies, a series of dependent and possibly censored failure times may be observed. Suppose that the failure times have a common continuous distribution function F. A popular stochastic measure of the distance between the density function f of the failure times and its kernel estimate fn is the integrated square error(ISE). In this article, we derive a central limit theorem for the integrated square error of the kernel density estimators under a censored dependent model.
Keywords
, , mixing; Bandwidth; Censored dependent data; Integrated square error; Kaplan–Meier estimator; Kernel density estimator.
@article{paperid:1026943,
author = {Jomhoori, Sara and Fakoor, Vahid and Azarnoosh, Hassan Ali},
title = {Central Limit Theorem for ISE of Kernel Density Estimators in Censored Dependent Model},
journal = {Communications in Statistics - Theory and Methods},
year = {2012},
volume = {41},
number = {8},
month = {March},
issn = {0361-0926},
pages = {1334--1349},
numpages = {15},
keywords = {-mixing; Bandwidth; Censored dependent data; Integrated square
error; Kaplan–Meier estimator; Kernel density estimator.},
}
%0 Journal Article
%T Central Limit Theorem for ISE of Kernel Density Estimators in Censored Dependent Model
%A Jomhoori, Sara
%A Fakoor, Vahid
%A Azarnoosh, Hassan Ali
%J Communications in Statistics - Theory and Methods
%@ 0361-0926
%D 2012
