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:
Online Access:http://psasir.upm.edu.my/id/eprint/11294/
http://psasir.upm.edu.my/id/eprint/11294/1/FSAS_1997_3_A.pdf
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author S.M.Baklizi, Ayman
author_facet S.M.Baklizi, Ayman
author_sort S.M.Baklizi, Ayman
building UPM Institutional Repository
collection Online Access
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
first_indexed 2025-11-15T07:45:58Z
format Thesis
id upm-11294
institution Universiti Putra Malaysia
institution_category Local University
language English
English
last_indexed 2025-11-15T07:45:58Z
publishDate 1997
recordtype eprints
repository_type Digital Repository
spelling upm-112942014-05-16T09:30:07Z http://psasir.upm.edu.my/id/eprint/11294/ Likelihood Inference In Parallel Systems Regression Models With Censored Data 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 1997 Thesis NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/11294/1/FSAS_1997_3_A.pdf S.M.Baklizi, Ayman (1997) Likelihood Inference In Parallel Systems Regression Models With Censored Data. PhD thesis, Universiti Putra Malaysia. Inference Censored observations (Statistics) English
spellingShingle Inference
Censored observations (Statistics)
S.M.Baklizi, Ayman
Likelihood Inference In Parallel Systems Regression Models With Censored Data
title 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_short Likelihood Inference In Parallel Systems Regression Models With Censored Data
title_sort likelihood inference in parallel systems regression models with censored data
topic Inference
Censored observations (Statistics)
url http://psasir.upm.edu.my/id/eprint/11294/
http://psasir.upm.edu.my/id/eprint/11294/1/FSAS_1997_3_A.pdf