A Bayes linear bayes method for estimation of correlated event rates

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<title>Bayes linear bayes method for estimation of correlated event rates</title>
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<abstract displayLabel="Summary">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.</abstract>
<note type="statement of responsibility">John Quigley...[et.al]</note>
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<title>Risk analysis : an international journal</title>
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<publisher>McLean, Virginia : Society for Risk Analysis, 1987-2015</publisher>
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<identifier type="issn">0272-4332</identifier>
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<text>02/12/2013 Volumen 33 Número 12 - diciembre 2013 </text>
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