Consequences of mapping data or parameters in Bayesian common-cause analysis

Corwin L. Atwood

Reliability Engineering and System Safety, 2013, vol. 118, issue C, 118-131

Abstract: When mapping the common-cause alpha factor model from a group of one size to one of another size, the following facts are shown: (1) mapping data down and treating the mapped data like observed data is much too conservative; (2) mapping alpha factors down puts restrictions on the resulting alphas, so their joint distribution cannot be Dirichlet; (3) if the mapped alpha factors' posterior distributions are moderately bell-shaped, the joint distribution can be approximated well by using correlated logistic-normal conditional probabilities and (4) Bayesian mapping up is possible, but highly sensitive to the prior distribution in the top group.

Keywords: Alpha factors; Logistic-normal distribution; Logit-normal distribution; Cholesky decomposition; Dirichlet prior (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:118:y:2013:i:c:p:118-131

DOI: 10.1016/j.ress.2013.04.015

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