Likelihood Inference In Parallel Systems Regression Models With Censored Data

S.M.Baklizi, Ayman (1997) Likelihood Inference In Parallel Systems Regression Models With Censored Data. PhD thesis, Universiti Putra Malaysia.

[img] PDF


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

Item Type:Thesis (PhD)
Subject:Censored observations (Statistics)
Chairman Supervisor:Associate Professor Dr. Isa Daud
Call Number:FSAS 1997 3
Faculty or Institute:Faculty of Environmental Studies
ID Code:11294
Deposited By: Mohd Nezeri Mohamad
Deposited On:18 Jul 2011 01:33
Last Modified:16 May 2014 09:30

Repository Staff Only: Edit item detail

Document Download Statistics

This item has been downloaded for since 18 Jul 2011 01:33.

View statistics for "Likelihood Inference In Parallel Systems Regression Models With Censored Data "

Universiti Putra Malaysia Institutional Repository

Universiti Putra Malaysia Institutional Repository is an on-line digital archive that serves as a central collection and storage of scientific information and research at the Universiti Putra Malaysia.

Currently, the collections deposited in the IR consists of Master and PhD theses, Master and PhD Project Report, Journal Articles, Journal Bulletins, Conference Papers, UPM News, Newspaper Cuttings, Patents and Inaugural Lectures.

As the policy of the university does not permit users to view thesis in full text, access is only given to the first 24 pages only.