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Maximum likelihood and Bayesian inference for common-cause of failure model

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  • Nguyen, H.D.
  • Gouno, E.

Abstract

This paper considers the statistical analysis of the Binomial Failure Rate (BFR) common-cause model in detail. Computational aspects of maximum likelihood and Bayesian methods are investigated. An Expectation-maximization (EM) algorithm to obtain maximum likelihood estimates is suggested to deal with missing data inherent for common-cause failures. A Bayesian approach is developed and the modified-Beta distribution is defined to characterize the posterior distribution for one of the model parameters. The different methods are applied and compared on both simulated and real data.

Suggested Citation

  • Nguyen, H.D. & Gouno, E., 2019. "Maximum likelihood and Bayesian inference for common-cause of failure model," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 56-62.
    • Handle: RePEc:eee:reensy:v:182:y:2019:i:c:p:56-62
    DOI: 10.1016/j.ress.2018.10.003
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    References listed on IDEAS

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    1. Atwood, Corwin L. & Kelly, Dana L., 2009. "The binomial failure rate common-cause model with WinBUGS," Reliability Engineering and System Safety, Elsevier, vol. 94(5), pages 990-999.
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    Cited by:

    1. Nguyen, H.D. & Gouno, E., 2020. "Bayesian inference for Common cause failure rate based on causal inference with missing data," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    2. Shekhar, Chandra & Kumar, Amit & Varshney, Shreekant, 2020. "Load sharing redundant repairable systems with switching and reboot delay," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    3. Bao, Han & Zhang, Hongbin & Shorthill, Tate & Chen, Edward & Lawrence, Svetlana, 2023. "Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Jayaraman, Deepan & Ramu, Palaniappan, 2023. "L-moments and Bayesian inference for probabilistic risk assessment with scarce samples that include extremes," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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