Bayesian estimation of a competing risk model based on Weibull and exponential distributions under right censored data

Talhi Hamida (), Aiachi Hiba () and Rahmania Nadji ()
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Talhi Hamida: Probability Statistics Laboratory, Department of Mathematics, Badji Mokhtar University, BP12, 23000 Annaba, Algeria
Aiachi Hiba: Probability Statistics Laboratory, Department of Mathematics, Badji Mokhtar University, BP12, 23000 Annaba, Algeria
Rahmania Nadji: Paul Painlevé Laboratory, UMR-CNRS 8524, Lille University, 59655 Villeneuve d’Ascq Cédex, France

Monte Carlo Methods and Applications, 2022, vol. 28, issue 2, 163-174

Abstract: In this paper, we investigate the estimation of the unknown parameters of a competing risk model based on a Weibull distributed decreasing failure rate and an exponentially distributed constant failure rate, under right censored data. The Bayes estimators and the corresponding risks are derived using various loss functions. Since the posterior analysis involves analytically intractable integrals, we propose a Monte Carlo method to compute these estimators. Given initial values of the model parameters, the maximum likelihood estimators are computed using the expectation-maximization algorithm. Finally, we use Pitman’s closeness criterion and integrated mean-square error to compare the performance of the Bayesian and the maximum likelihood estimators.

Keywords: Weibull model; exponential model; right censored sample; Bayesian estimations; expectation maximization algorithm; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/mcma-2022-2112

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