Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model

In a conventional competing risk s model, the time to failure of a particular experimental unit might be censored and the cause of failure can be known or unknown. In this thesis the analysis of this particular model was based on the cause-specific hazard of Cox model. The Expectation Maximizatio...

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Main Author:	Elfaki, Faiz. A. M.
Format:	Thesis
Language:	English English
Published:	2000
Subjects:	Proportional Hazards Models Competing risks - Measurement
Online Access:	http://psasir.upm.edu.my/id/eprint/9552/1/FSAS_2000_5_A.pdf
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spelling	my-upm-ir.95522013-09-26T01:07:56Z Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model 2000-06 Elfaki, Faiz. A. M. In a conventional competing risk s model, the time to failure of a particular experimental unit might be censored and the cause of failure can be known or unknown. In this thesis the analysis of this particular model was based on the cause-specific hazard of Cox model. The Expectation Maximization (EM) was considered to obtain the estimate of the parameters. These estimates were then compared to the Newton-Raphson iteration method. A generated data where the failure times were taken as exponentially distributed was used to further compare these two methods of estimation. From the simulation study for this particular case, we can conclude that the EM algorithm proved to be more superior in terms of mean value of parameter estimates, bias and root mean square error. To detect irregularities and peculiarities in the data set, the residuals, Cook distance and the likelihood distance were computed. Unlike the residuals, the perturbation method of Cook's distance and the likelihood distance were effective in the detection of observations that have influenced on the parameter estimates. We considered both the EM approach and the ordinary maximum likelihood estimation (MLE) approach in the computation of the influence measurements. For the ultimate results of influence measurements we utilized the approach of the one step . The EM one-step and the maximum likelihood (ML) one-step gave conclusions that are analogous to the full iteration distance measurements. In comparison, it was found that EM one-step gave better results than the ML one step with respect to the value of Cook's distance and likelihood distance. It was also found that Cook's distance i s better than the likelihood distance with respect to the number of observations detected. Proportional Hazards Models Competing risks - Measurement 2000-06 Thesis http://psasir.upm.edu.my/id/eprint/9552/ http://psasir.upm.edu.my/id/eprint/9552/1/FSAS_2000_5_A.pdf application/pdf en public masters Universiti Putra Malaysia Proportional Hazards Models Competing risks - Measurement Faculty of Science and Environmental Studies English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
topic	Proportional Hazards Models Competing risks - Measurement
spellingShingle	Proportional Hazards Models Competing risks - Measurement Elfaki, Faiz. A. M. Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model
description	In a conventional competing risk s model, the time to failure of a particular experimental unit might be censored and the cause of failure can be known or unknown. In this thesis the analysis of this particular model was based on the cause-specific hazard of Cox model. The Expectation Maximization (EM) was considered to obtain the estimate of the parameters. These estimates were then compared to the Newton-Raphson iteration method. A generated data where the failure times were taken as exponentially distributed was used to further compare these two methods of estimation. From the simulation study for this particular case, we can conclude that the EM algorithm proved to be more superior in terms of mean value of parameter estimates, bias and root mean square error. To detect irregularities and peculiarities in the data set, the residuals, Cook distance and the likelihood distance were computed. Unlike the residuals, the perturbation method of Cook's distance and the likelihood distance were effective in the detection of observations that have influenced on the parameter estimates. We considered both the EM approach and the ordinary maximum likelihood estimation (MLE) approach in the computation of the influence measurements. For the ultimate results of influence measurements we utilized the approach of the one step . The EM one-step and the maximum likelihood (ML) one-step gave conclusions that are analogous to the full iteration distance measurements. In comparison, it was found that EM one-step gave better results than the ML one step with respect to the value of Cook's distance and likelihood distance. It was also found that Cook's distance i s better than the likelihood distance with respect to the number of observations detected.
format	Thesis
qualification_level	Master's degree
author	Elfaki, Faiz. A. M.
author_facet	Elfaki, Faiz. A. M.
author_sort	Elfaki, Faiz. A. M.
title	Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model
title_short	Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model
title_full	Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model
title_fullStr	Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model
title_full_unstemmed	Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model
title_sort	em approach on influence measures in competing risks via proportional hazard regression model
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Science and Environmental Studies
publishDate	2000
url	http://psasir.upm.edu.my/id/eprint/9552/1/FSAS_2000_5_A.pdf
_version_	1747810980057841664

Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model

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