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

作     者：Ranjan, Rakesh Sen, Rijji Upadhyay, Satyanshu K. 

作者机构：Banaras Hindu Univ DST Ctr Interdisciplinary Math Sci Varanasi Uttar Pradesh India Calcutta Univ Behala Coll Dept Stat Kolkata W Bengal India Banaras Hindu Univ Dept Stat Varanasi Uttar Pradesh India 

出 版 物：《RELIABILITY ENGINEERING & SYSTEM SAFETY》 (可靠性工程与系统安全)

年 卷 期：2021年第214卷

页      面：107747-107747页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0837[工学-安全科学与工程] 

基　　金：The authors acknowledge their gratitude to the editor and the anonymous reviewers for their valuable and constructive suggestions that helped in drastically improving the earlier version of themanuscript 

主　　题：Left truncated right censored data Weibull distribution Gamma distribution Lognormal distribution Weakly informative prior Metropolis algorithm Hamiltonian Monte Carlo Bayes factor Bridge sampling 

摘      要：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.