A Bayes linear Bayes method for estimation of correlated event rates

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Abstract

Typically, full Bayesian estimation of correlated event rates can be computationally challenging since estimators are intractable. When estimation of event rates represents one activity within a larger modeling process, there is an incentive to develop more efficient inference than provided by a full Bayesian model. We develop a new subjective inference method for correlated event rates based on a Bayes linear Bayes model under the assumption that events are generated from a homogeneous Poisson process. To reduce the elicitation burden we introduce homogenization factors to the model and, as an alternative to a subjective prior, an empirical method using the method of moments is developed. Inference under the new method is compared against estimates obtained under a full Bayesian model, which takes a multivariate gamma prior, where the predictive and posterior distributions are derived in terms of well-known functions. The mathematical properties of both models are presented. A simulation study shows that the Bayes linear Bayes inference method and the full Bayesian model provide equally reliable estimates. An illustrative example, motivated by a problem of estimating correlated event rates across different users in a simple supply chain, shows how ignoring the correlation leads to biased estimation of event rates.
Original languageEnglish
Pages (from-to)2209–2224
Number of pages16
JournalRisk Analysis
Volume33
Issue number12
Early online date28 Mar 2013
DOIs
Publication statusPublished - Dec 2013

Keywords

  • reliability
  • correlated event rates
  • poisson process
  • bayes linear kinematics
  • empirical bayes
  • supply chain

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