Title : ( Asymptotic expansion for ISE of kernel density estimators under censored dependent model )
Authors: Sara Jomhoori , Vahid Fakoor , Hassan Ali Azarnoosh ,
Full TextAbstract
In some long term studies, we encounter a series of dependent and censored observations. Randomly censored data consist of i.i.d. pairs of observations .Xi; i/i D 1; : : : ; n. If i D 0, Xi denotes a censored observation, and if i D 1, Xi denotes a survival time, which is the variable of interest. One of the global stochastic measures of the distance between a density and its kernel density estimator is integrated square error. In this paper, we apply the technique of strong approximation to establish an asymptotic expansion for the integrated square error of the kernel density estimate, when censored data are showing some kind of dependence
Keywords
, Dependent Data, Randomly censored data, Kernel density estimator, Integrated square error. Strong approximation, Strong mixing
@article{paperid:1011409,
author = {Jomhoori, Sara and Fakoor, Vahid and Azarnoosh, Hassan Ali},
title = {Asymptotic expansion for ISE of kernel density estimators under censored dependent model},
journal = {Statistics and Probability Letters},
year = {2009},
volume = {79},
number = {17},
month = {May},
issn = {0167-7152},
pages = {1809--1817},
numpages = {8},
keywords = {Dependent Data; Randomly censored data; Kernel density estimator; Integrated square error. Strong approximation; Strong mixing},
}
%0 Journal Article
%T Asymptotic expansion for ISE of kernel density estimators under censored dependent model
%A Jomhoori, Sara
%A Fakoor, Vahid
%A Azarnoosh, Hassan Ali
%J Statistics and Probability Letters
%@ 0167-7152
%D 2009
