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 functi...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.展开更多
基金Supported by the Natural Science Foundation of China(11401341,11271136 and 81530086)111 Project(B14019)+2 种基金Natural Science Foundation of Fujian Province,China(2015J05014,2016J01681 and 2017N0029)Scientific Research Training Program of Fujian Province University for Distinguished Young Scholar(2015)New Century Excellent Talents Support Project of Fujian Province University([2016]23)
文摘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.
