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

Author

Listed:
  • Talhi Hamida
  • 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)

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.

Suggested Citation

  • Talhi Hamida & Aiachi Hiba & Rahmania Nadji, 2022. "Bayesian estimation of a competing risk model based on Weibull and exponential distributions under right censored data," Monte Carlo Methods and Applications, De Gruyter, vol. 28(2), pages 163-174, June.
    • Handle: RePEc:bpj:mcmeap:v:28:y:2022:i:2:p:163-174:n:7
    DOI: 10.1515/mcma-2022-2112
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