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A Bayesian Analysis of Reliability in Accelerated Life Tests Using Gibbs Sampler

Author

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  • Néli Maria Costa Mattos

    (Institute Militar de Engenharia)

  • Hélio Santos Migon

    (Universidade Federal do Rio de Janeiro)

Abstract

Summary In this paper MCMC (Markov Chain Monte Carlo) techniques are proposed to perform Bayesian inference to evaluate the reliability of units with Weibull lifetime, submitted to accelerated and censored life tests. A full Bayesian analysis is done via Gibbs sampling. The marginal posterior of the main parameters and other unobserved quantities of interest derived from them are obtained. Two numerical applications using real and artificially generated data are discussed.

Suggested Citation

  • Néli Maria Costa Mattos & Hélio Santos Migon, 2001. "A Bayesian Analysis of Reliability in Accelerated Life Tests Using Gibbs Sampler," Computational Statistics, Springer, vol. 16(2), pages 299-312, July.
    • Handle: RePEc:spr:compst:v:16:y:2001:i:2:d:10.1007_s001800100066
    DOI: 10.1007/s001800100066
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    References listed on IDEAS

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    1. P. Dellaportas & A. F. M. Smith, 1993. "Bayesian Inference for Generalized Linear and Proportional Hazards Models Via Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(3), pages 443-459, September.
    2. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
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    Cited by:

    1. Carlos Pérez-González & Arturo Fernández, 2013. "Classical versus Bayesian risks in acceptance sampling: a sensitivity analysis," Computational Statistics, Springer, vol. 28(3), pages 1333-1350, June.

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