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Bayesian analysis for step-stress accelerated life testing using weibull proportional hazard model

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Abstract

In this paper, we present a Bayesian analysis for the Weibull proportional hazard (PH) model used in step-stress accelerated life testings. The key mathematical and graphical difference between the Weibull cumulative exposure (CE) model and the PH model is illustrated. Compared with the CE model, the PH model provides more flexibility in fitting step-stress testing data and has the attractive mathematical properties of being desirable in the Bayesian framework. A Markov chain Monte Carlo algorithm with adaptive rejection sampling technique is used for posterior inference. We demonstrate the performance of this method on both simulated and real datasets.

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Acknowledgments

This research is partially supported by NSF/CMMI-0654417. Sha’s work was also partially supported by Shanghai High Education 085 Project Fund.

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Authors and Affiliations

  1. Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX, 79968, USA

    Naijun Sha

  2. Business Information Management School, Shanghai Institute of Foreign Trade, Shanghai, 201620, China

    Naijun Sha

  3. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287, USA

    Rong Pan

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  1. Naijun Sha
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  2. Rong Pan
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Correspondence to Naijun Sha.

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Sha, N., Pan, R. Bayesian analysis for step-stress accelerated life testing using weibull proportional hazard model. Stat Papers 55, 715–726 (2014). https://doi.org/10.1007/s00362-013-0521-2

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  • DOI: https://doi.org/10.1007/s00362-013-0521-2

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