Bayesian reference analysis for proportional hazards model of random censorship with Weibull distribution

Title:

Bayesian reference analysis for proportional hazards model of random censorship with Weibull distribution

Creator:

Ajmal, Maria, Danish, Muhammad Yameen, and Tahira, Ayesha

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:8adaaa8c-e950-41b7-87cc-ae97b0566d8a
uuid:8adaaa8c-e950-41b7-87cc-ae97b0566d8a
doi:10.14736/kyb-2022-1-0025

Subject:

Jeffreys prior method, reference prior method, random censorship model, Kaplan-Meier survival estimate, and Henze goodness-of-fit test

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

25-42

Source:

Kybernetika | 2022 Volume:58 | Number:1

Harvested from:

CDK

Metadata only:

false