Titre :

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

Auteur(s) :

Talhi, Hamida [Auteur]
Aiachi, Hiba [Auteur]
Rahmania, Nadji [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]

Titre de la revue :

Monte Carlo Methods and Applications

Pagination :

163-174

Éditeur :

De Gruyter

Date de publication :

2022-06-01

ISSN :

0929-9629

Discipline(s) HAL :

Mathématiques [math]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL

Date de dépôt :

2025-01-24T14:44:07Z