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

作　　者：Qiang GUAN Yin-cai TANG 

机构地区：[1]Institute of Information Engineering, Sanming University, Sanming 365004, China [2]School of Finance and Statistics, East China Normal University, Shanghai 200241, China

出　　处：《Acta Mathematicae Applicatae Sinica》2018年第1期51-64,共14页应用数学学报（英文版）

基　　金：Supported by the Natural Science Foundation of China(11401341,11271136 and 81530086);111 Project(B14019);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)

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.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.

关 键 词：accelerated life testing Bayesian approach Gibbs sampling type-I censoring log-location-scale distributions optimal design. 

分 类 号：O1[理学—数学]