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dc.contributor.authorSimpson, Daniel
dc.contributor.authorRue, Håvard
dc.contributor.authorRiebler, Andrea Ingeborg
dc.contributor.authorMartins, Thiago Guerrera
dc.contributor.authorSørbye, Sigrunn Holbek
dc.date.accessioned2019-01-28T15:45:31Z
dc.date.available2019-01-28T15:45:31Z
dc.date.created2017-04-07T12:05:38Z
dc.date.issued2017
dc.identifier.citationStatistical Science. 2017, 32 (1), 1-28.nb_NO
dc.identifier.issn0883-4237
dc.identifier.urihttp://hdl.handle.net/11250/2582705
dc.description.abstractIn this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a base model. Proper priors are defined to penalise the complexity induced by deviating from the simpler base model and are formulated after the input of a user-defined scaling parameter for that model component, both in the univariate and the multivariate case. These priors are invariant to reparameterisations, have a natural connection to Jeffreys’ priors, are designed to support Occam’s razor and seem to have excellent robustness properties, all which are highly desirable and allow us to use this approach to define default prior distributions. Through examples and theoretical results, we demonstrate the appropriateness of this approach and how it can be applied in various situations.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Mathematical Statisticsnb_NO
dc.titlePenalising Model Component Complexity: A Principled, Practical Approach to Constructing Priorsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber1-28nb_NO
dc.source.volume32nb_NO
dc.source.journalStatistical Sciencenb_NO
dc.source.issue1nb_NO
dc.identifier.doi10.1214/16-STS576
dc.identifier.cristin1464342
dc.relation.projectNorges forskningsråd: 240873nb_NO
dc.description.localcodeThis is the authors' accepted and reviewed manuscript of the article. © Institute of Mathematical Statistics, 2017nb_NO
cristin.unitcode194,63,15,0
cristin.unitnameInstitutt for matematiske fag
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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