Likelihood Inference In Parallel Systems Regression Models With Censored Data

The work in this thesis is concerned with the investigation of the finite sample performance of asymptotic inference procedures based on the likelihood function when applied to the regression model based on parallel systems with censored data. The study includes investigating the adequacy of thes...

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Main Author:	S.M.Baklizi, Ayman
Format:	Thesis
Language:	English English
Published:	1997
Subjects:	Inference Censored observations (Statistics)
Online Access:	http://psasir.upm.edu.my/id/eprint/11294/1/FSAS_1997_3_A.pdf
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id	my-upm-ir.11294
record_format	uketd_dc
spelling	my-upm-ir.112942014-05-16T09:30:07Z Likelihood Inference In Parallel Systems Regression Models With Censored Data 1997 S.M.Baklizi, Ayman The work in this thesis is concerned with the investigation of the finite sample performance of asymptotic inference procedures based on the likelihood function when applied to the regression model based on parallel systems with censored data. The study includes investigating the adequacy of these inferential procedures as well as investigating the relative performances of asymptotically equivalent likelihood-based statistics in small samples. The maximum likelihood estimator of the parameters of this model is not available in closed form. Thus, its actual sampling distribution is intractable. A simulation study is conducted to investigate the bias, the finite sample variance, the asymptotic variance obtained from the inverse of the observed Fisher information matrix, the adequacy of this approximate asymptotic variance, and the mean squared Inference Censored observations (Statistics) 1997 Thesis http://psasir.upm.edu.my/id/eprint/11294/ http://psasir.upm.edu.my/id/eprint/11294/1/FSAS_1997_3_A.pdf application/pdf en public phd doctoral Universiti Putra Malaysia Inference Censored observations (Statistics) Faculty of Environmental studies English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
topic	Inference Censored observations (Statistics)
spellingShingle	Inference Censored observations (Statistics) S.M.Baklizi, Ayman Likelihood Inference In Parallel Systems Regression Models With Censored Data
description	The work in this thesis is concerned with the investigation of the finite sample performance of asymptotic inference procedures based on the likelihood function when applied to the regression model based on parallel systems with censored data. The study includes investigating the adequacy of these inferential procedures as well as investigating the relative performances of asymptotically equivalent likelihood-based statistics in small samples. The maximum likelihood estimator of the parameters of this model is not available in closed form. Thus, its actual sampling distribution is intractable. A simulation study is conducted to investigate the bias, the finite sample variance, the asymptotic variance obtained from the inverse of the observed Fisher information matrix, the adequacy of this approximate asymptotic variance, and the mean squared
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	S.M.Baklizi, Ayman
author_facet	S.M.Baklizi, Ayman
author_sort	S.M.Baklizi, Ayman
title	Likelihood Inference In Parallel Systems Regression Models With Censored Data
title_short	Likelihood Inference In Parallel Systems Regression Models With Censored Data
title_full	Likelihood Inference In Parallel Systems Regression Models With Censored Data
title_fullStr	Likelihood Inference In Parallel Systems Regression Models With Censored Data
title_full_unstemmed	Likelihood Inference In Parallel Systems Regression Models With Censored Data
title_sort	likelihood inference in parallel systems regression models with censored data
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Environmental studies
publishDate	1997
url	http://psasir.upm.edu.my/id/eprint/11294/1/FSAS_1997_3_A.pdf
_version_	1747811235814965248

Likelihood Inference In Parallel Systems Regression Models With Censored Data

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