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Title: Bayesian reference analysis for proportional hazards model of random censorship with Weibull distribution (English)
Author: Ajmal, Maria
Author: Danish, Muhammad Yameen
Author: Tahira, Ayesha
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 58
Issue: 1
Year: 2022
Pages: 25-42
Summary lang: English
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Category: math
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Summary: This article deals with the objective Bayesian analysis of random censorship model with informative censoring using Weibull distribution. The objective Bayesian analysis has a long history from Bayes and Laplace through Jeffreys and is reaching the level of sophistication gradually. The reference prior method of Bernardo is a nice attempt in this direction. The reference prior method is based on the Kullback-Leibler divergence between the prior and the corresponding posterior distribution and easy to implement when the information matrix exists in closed-form. We apply this method to Weibull random censorship model and compare it with Jeffreys and maximum likelihood methods. It is observed that the closed-form expressions for the Bayes estimators are not possible; we use importance sampling technique to obtain the approximate Bayes estimates. The behaviour of maximum likelihood and Bayes estimators is observed via extensive numerical simulation. The proposed methodology is used for the analysis of a real-life data for illustration and appropriateness of the model is tested by Henze goodness-of-fit test. (English)
Keyword: Jeffreys prior method
Keyword: reference prior method
Keyword: random censorship model
Keyword: Kaplan–Meier survival estimate
Keyword: Henze goodness-of-fit test
MSC: 62F10
MSC: 62F15
MSC: 62N01
MSC: 62N05
idZBL: Zbl 07511609
idMR: MR4405945
DOI: 10.14736/kyb-2022-1-0025
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Date available: 2022-04-08T07:48:03Z
Last updated: 2022-08-11
Stable URL: http://hdl.handle.net/10338.dmlcz/149600
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