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Bayes factors for accelerated life testing models
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Type
Academic journal
Author
Neill Smit (North-West University) Lizanne Raubenheimer (Rhodes University)
Journal
한국통계학회 CSAM(Communications for Statistical Applications and Methods) Vol.29 No.5 KCI Accredited Journals SCOPUS
Published
2022.9
Pages
513 - 532 (20page)

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Bayes factors for accelerated life testing models
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In this paper, the use of Bayes factors and the deviance information criterion for model selection are compared in a Bayesian accelerated life testing setup. In Bayesian accelerated life testing, the most used tool for model comparison is the deviance information criterion. An alternative and more formal approach is to use Bayes factors to compare models. However, Bayesian accelerated life testing models with more than one stressor often have mathematically intractable posterior distributions and Markov chain Monte Carlo methods are employed to obtain posterior samples to base inference on. The computation of the marginal likelihood is challenging when working with such complex models. In this paper, methods for approximating the marginal likelihood and the application thereof in the accelerated life testing paradigm are explored for dual-stress models. A simulation study is also included, where Bayes factors using the different approximation methods and the deviance information are compared.
#accelerated life testing #Bayes factors #generalized Eyring model #Markov chain Monte Carlo #model comparison

Contents

Abstract
1. Introduction
2. Bayesian model selection
3. Estimating the marginal likelihood
4. Generalized Eyring ALT models
5. Application
6. Simulation study
7. Conclusions
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