TY - JOUR
T1 - Exponential progressive step-stress life-testing with link function based on Box-Cox transformation
AU - Fan, Tsai Hung
AU - Wang, Wan Lun
AU - Balakrishnan, N.
N1 - Funding Information:
We gratefully acknowledge the referees for their valuable comments, which substantially improved the quality of the paper. This research work was supported by the National Science Council under Grant number NSC 95-2118-M-008-001 of Taiwan.
PY - 2008/8/1
Y1 - 2008/8/1
N2 - In order to quickly extract information on the life of a product, accelerated life-tests are usually employed. In this article, 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 is 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.
AB - In order to quickly extract information on the life of a product, accelerated life-tests are usually employed. In this article, 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 is 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.
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U2 - 10.1016/j.jspi.2007.10.002
DO - 10.1016/j.jspi.2007.10.002
M3 - Article
AN - SCOPUS:42149119528
SN - 0378-3758
VL - 138
SP - 2340
EP - 2354
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
IS - 8
ER -