Improved new modified Weibull distribution: A Bayes study using Hamiltonian Monte Carlo simulation

Tien Thanh Thach and Radim Bris

Journal of Risk and Reliability, 2020, vol. 234, issue 3, 496-511

Abstract: The newly modified Weibull distribution defined in the literature is a model based on combining the Weibull and modified Weibull distributions. It has been demonstrated as the best model for fitting to the bathtub-shaped failure rate data sets. However, another new model based on combining the modified Weibull and Gompertz distributions has been demonstrated later to be even better than the first model. In this article, we have shown how to improve the former model into a better model, and more importantly, we have provided a full Bayesian analysis of the improved model. The Hamiltonian Monte Carlo and cross-entropy methods have been exploited to empower the traditional methods of statistical estimation. Bayes estimators have been obtained using Hamiltonian Monte Carlo for posterior simulations. Bayesian model checking has also been provided in order to check the validation of the model when fitting to real data sets. We have also provided the maximum likelihood estimators of the model parameters using the cross-entropy method to optimize the log-likelihood function. The results derived from the analysis of two well-known data sets show that the improved model is much better than its original form.

Keywords: Improved new modified Weibull model; Bayesian estimators; Hamiltonian Monte Carlo; Bayesian model checking; cross-entropy method; maximum likelihood estimators; Aarset data; Meeker–Escobar data (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:234:y:2020:i:3:p:496-511

DOI: 10.1177/1748006X19896740

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