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dc.contributor.authorLee, Jeong Eun*
hal.structure.identifierDepartment of Statistics [Oxford]
dc.contributor.authorNicholls, Geoff K*
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorRyder, Robin J.*
dc.date.accessioned2019-12-20T11:10:58Z
dc.date.available2019-12-20T11:10:58Z
dc.date.issued2019
dc.identifier.issn1931-6690
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20364
dc.language.isoenen
dc.subjectMonte Carloen
dc.subjectapproximationen
dc.subjectcalibrationen
dc.subjectcredible intervalsen
dc.subject.ddc519en
dc.titleCalibration procedures for approximate Bayesian credible setsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe develop and apply two calibration procedures for checking the coverage of approximate Bayesian credible sets, including intervals estimated using Monte Carlo methods. The user has an ideal prior and likelihood, but generates a credible set for an approximate posterior based on some approximate prior and likelihood. We estimate the realised posterior coverage achieved by the approximate credible set. This is the coverage of the unknown “true” parameter if the data are a realisation of the user’s ideal observation model conditioned on the parameter, and the parameter is a draw from the user’s ideal prior. In one approach we estimate the posterior coverage at the data by making a semi-parametric logistic regression of binary coverage outcomes on simulated data against summary statistics evaluated on simulated data. In another we use Importance Sampling from the approximate posterior, windowing simulated data to fall close to the observed data. We illustrate our methods on four examples.en
dc.relation.isversionofjnlnameBayesian Analysis
dc.relation.isversionofjnlvol14en
dc.relation.isversionofjnlissue4en
dc.relation.isversionofjnldate2019-10
dc.relation.isversionofjnlpages1245-1269en
dc.relation.isversionofdoi10.1214/19-BA1175en
dc.relation.isversionofjnlpublisherInternational Society for Bayesian Analysisen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2019-12-20T11:08:25Z
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