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

H.D. Nguyen and E. Gouno

Reliability Engineering and System Safety, 2019, vol. 182, issue C, 56-62

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.

Keywords: Binomial failure-rate model; Common-cause failure; Poisson process; Maximum likelihood; Bayesian estimation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:182:y:2019:i:c:p:56-62

DOI: 10.1016/j.ress.2018.10.003

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