Bayes analysis of some important lifetime models using MCMC based approaches when the observations are left truncated and right censored

Rakesh Ranjan, Rijji Sen and Satyanshu K. Upadhyay

Reliability Engineering and System Safety, 2021, vol. 214, issue C

Abstract: The paper considers the Bayes analysis of important lifetime models such as the Weibull, the gamma, and the lognormal distributions when the available data are left truncated and right-censored. Weakly informative prior distributions are employed for the purpose. Two well-known Markov chain Monte Carlo based approaches, namely, the Metropolis algorithm and the Hamiltonian Monte Carlo technique are used to draw samples from analytically intractable posterior distributions. Besides, the paper does a comparative study of the three entertained models using Bayes factor. The paper has considered calculating the marginal likelihood using bridge sampler algorithm for evaluating the necessary Bayes factor. Finally, a numerical illustration based on a real dataset compares the two algorithms and draws relevant conclusions appropriately.

Keywords: Left truncated right censored data; Weibull distribution; Gamma distribution; Lognormal distribution; Weakly informative prior; Metropolis algorithm; Hamiltonian Monte Carlo; Bayes factor; Bridge sampling (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021002751

DOI: 10.1016/j.ress.2021.107747

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