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Objective bayesian analysis for multiple repairable systems

Author

Listed:
  • Amanda M E D’Andrea
  • Vera L D Tomazella
  • Hassan M Aljohani
  • Pedro L Ramos
  • Marco P Almeida
  • Francisco Louzada
  • Bruna A W Verssani
  • Amanda B Gazon
  • Ahmed Z Afify

Abstract

This article focus on the analysis of the reliability of multiple identical systems that can have multiple failures over time. A repairable system is defined as a system that can be restored to operating state in the event of a failure. This work under minimal repair, it is assumed that the failure has a power law intensity and the Bayesian approach is used to estimate the unknown parameters. The Bayesian estimators are obtained using two objective priors know as Jeffreys and reference priors. We proved that obtained reference prior is also a matching prior for both parameters, i.e., the credibility intervals have accurate frequentist coverage, while the Jeffreys prior returns unbiased estimates for the parameters. To illustrate the applicability of our Bayesian estimators, a new data set related to the failures of Brazilian sugar cane harvesters is considered.

Suggested Citation

  • Amanda M E D’Andrea & Vera L D Tomazella & Hassan M Aljohani & Pedro L Ramos & Marco P Almeida & Francisco Louzada & Bruna A W Verssani & Amanda B Gazon & Ahmed Z Afify, 2021. "Objective bayesian analysis for multiple repairable systems," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-19, November.
    • Handle: RePEc:plo:pone00:0258581
    DOI: 10.1371/journal.pone.0258581
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    References listed on IDEAS

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    1. Mark Berman & T. Rolf Turner, 1992. "Approximating Point Process Likelihoods with Glim," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 31-38, March.
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