Exponential progressive step-stress life-testing with link function based on Box-Cox transformation

Tsai Hung Fan, Wan Lun Wang, N. Balakrishnan

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)2340-2354
Number of pages15
JournalJournal of Statistical Planning and Inference
Volume138
Issue number8
DOIs
StatePublished - 1 Aug 2008

Keywords

  • Accelerated life-testing
  • Bayesian inference
  • Box-Cox transformation
  • Cumulative exposure model
  • Fisher-scoring algorithm
  • Link function
  • Markov chain Monte Carlo
  • Maximum likelihood estimates
  • Progressive censoring
  • Step-stress test

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