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dc.contributor.authorGelman, Andrew
dc.contributor.authorRobert, Christian P.
dc.contributor.authorRousseau, Judith
dc.date.accessioned2015-04-14T17:40:03Z
dc.date.available2015-04-14T17:40:03Z
dc.date.issued2013
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/14944
dc.language.isoenen
dc.subjectFoundationsen
dc.subjectBayes factoren
dc.subjectBayesian model choiceen
dc.subjecttesting of hypothesesen
dc.subjectimproper priorsen
dc.subject.ddc519en
dc.titleInherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkinen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenFor many decades, statisticians have made attempts to prepare the Bayesian omelette without breaking the Bayesian eggs; that is, to obtain probabilistic likelihood-based inferences without relying on informative prior distributions. A recent example is Murray Aitkin´s recent book, Statistical Inference, which presents an approach to statistical hypothesis testing based on comparisons of posterior distributions of likelihoods under competing models. Aitkin develops and illustrates his method using some simple examples of inference from iid data and two-way tests of independence. We analyze in this note some consequences of the inferential paradigm adopted therein, discussing why the approach is incompatible with a Bayesian perspective and why we do not find it relevant for applied work.en
dc.relation.isversionofjnlnameStatistics & Risk Modeling
dc.relation.isversionofjnlvol30en
dc.relation.isversionofjnlissue2en
dc.relation.isversionofjnldate2013
dc.relation.isversionofjnlpages105-120en
dc.relation.isversionofdoihttp://dx.doi.org/10.1524/strm.2013.1113en
dc.identifier.urlsitehttp://arxiv.org/abs/1012.2184v2en
dc.relation.isversionofjnlpublisherDe Gruyteren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen


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