Exponential Inference Models for Progressive Step-Stress Life Testing with Box-Cox Transformation

• Main Authors: Wan-Lun Wang, 王婉倫
• Other Authors: 內容為英文
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/13495859659147833282

碩士 === 國立中央大學 === 統計研究所 === 95 === In order to quickly extract information on the life of a product, accelerated life-tests are usually employed. In this thesis, we discuss a k-stage step-stress accelerated life-test with M stress variables when the underlying data are progressively Type-I group censored. The life-testing model assumed is an exponential distribution with a link function that relates the failure rate and the stress variables in a linear way under the Box-Cox transformation, and a cumulative exposure model for modelling the effect of stress changes. The classical maximum likelihood method as well as a fully Bayesian method based on the Markov chain Monte Carlo (MCMC) technique are developed for inference on all the parameters of this model. Numerical examples are presented to illustrate all the methods of inference developed here, and a comparison of the ML and Bayesian methods is also carried out.