Bayesian Planning of Optimal Step-stress Accelerated Life Test for Log-location-scale Distributions

2018年第01期

关键词:
accelerated life testing;Bayesian approach;Gibbs sampling;type-I censoring;log-location-scale distributions;optimal design

Keywords
accelerated life testing;Bayesian approach;Gibbs sampling;type-I censoring;log-location-scale distributions;optimal design
摘要
     This paper introduces some Bayesian optimal design methods for step-stress accelerated life test planning with one accelerating variable, when the acceleration model is linear in the accelerated variable or its function, based on censored data from a log-location-scale distributions. In order to find the optimal plan,we propose different Monte Carlo simulation algorithms for different Bayesian optimal criteria. We present an example using the lognormal life distribution with Type-I censoring to illustrate the different Bayesian methods and to examine the effects of the prior distribution and sample size. By comparing the different Bayesian methods we suggest that when the data have large(small) sample size B1(τ)(B2(τ)) method is adopted. Finally, the Bayesian optimal plans are compared with the plan obtained by maximum likelihood method.


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