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dc.contributor.authorSimpson, Daniel
dc.contributor.authorRue, Haavard
dc.contributor.authorRiebler, Andrea
dc.contributor.authorMartins, Thiago G.
dc.contributor.authorSørbye, Sigrunn H.
dc.date.accessioned2017-05-09T08:34:34Z
dc.date.available2017-05-09T08:34:34Z
dc.date.issued2017-04-06
dc.identifier.citationSimpson D, Rue H, Riebler A, Martins TG, Sørbye SH (2017) Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors. Statistical Science 32: 1–28. Available: http://dx.doi.org/10.1214/16-STS576.
dc.identifier.issn0883-4237
dc.identifier.doi10.1214/16-STS576
dc.identifier.urihttp://hdl.handle.net/10754/623413
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 repa-rameterisations, 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.
dc.description.sponsorshipThe authors are grateful to the Editor, Associate Editor and three anonymous referees for exceptionally helpful and constructive reports. The authors acknowledge Gianluca Baio, Haakon C. Bakka, Simon Barthelmé, Joris Bierkens, Sylvia Frühwirth-Schnatter, Geir-Arne Fuglstad, Nadja Klein, Thomas Kneib, Alex Lenkoski, Finn K. Lindgren, Christian P. Robert and Malgorzata Roos for stimulating discussions and comments related to this work.
dc.publisherInstitute of Mathematical Statistics
dc.relation.urlhttp://projecteuclid.org/euclid.ss/1491465621
dc.rightsArchived with thanks to Statistical Science
dc.subjectBayesian theory
dc.subjectinterpretable prior distributions
dc.subjecthierarchical models
dc.subjectdisease mapping
dc.subjectinformation geometry
dc.subjectprior on correlation matrices
dc.titlePenalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalStatistical Science
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Mathematical Sciences, University of Bath, Bath, BA2 7AY, , United Kingdom
dc.contributor.institutionDepartment of Mathematical Sciences, NTNU, , Norway
dc.contributor.institutionDepartment of Mathematics and Statistics, UiT The Arctic University of Norway, , Norway
dc.identifier.arxivid1403.4630
kaust.personRue, Haavard
refterms.dateFOA2018-04-06T00:00:00Z
dc.date.published-online2017-04-06
dc.date.published-print2017-02


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