The notion of "composite reliability" and its hierarchical Bayes estimation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

14 Scopus Citations
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Author(s)

  • Jingxian Chen
  • Nozer D. Singpurwalla

Detail(s)

Original languageEnglish
Pages (from-to)1474-1484
Journal / PublicationJournal of the American Statistical Association
Volume91
Issue number436
Publication statusPublished - Dec 1996
Externally publishedYes

Abstract

In this article we introduce the notion of "composite reliability" as a measure of the overall reliability of a collection of heterogeneous but similar items. We then propose a hierarchical Bayes model for estimating the composite reliability of systems whose lifelengths are expressed as binary random variables. Subsequently, we develop an alternative approach for the simultaneous inference about many small but related binomial parameters. We propose a two-stage prior for addressing problems of this type and use the Gibbs sampler algorithm for addressing problems involving small proportions. Our topic, though suggested by an issue pertaining to the safety of nuclear power plants, arises in other commonly occurring situations pertaining to consumerism, public policy, and government regulation. Our development generalizes for situations involving lifelengths that need not be binary.

Research Area(s)

  • Binomial inference, Empirical bayes, Exchangeability, Gibbs sampling, Markov chain monté carlo, Nuclear power, Public policy, Regulation, Risk analysis, Safety analysis, Simulation

Citation Format(s)

The notion of "composite reliability" and its hierarchical Bayes estimation. / Chen, Jingxian; Singpurwalla, Nozer D.
In: Journal of the American Statistical Association, Vol. 91, No. 436, 12.1996, p. 1474-1484.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review