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dc.contributor.authorFuglstad, Geir-Arne
dc.contributor.authorSimpson, Daniel
dc.contributor.authorLindgren, Finn
dc.contributor.authorRue, Håvard
dc.date.accessioned2019-03-28T13:53:04Z
dc.date.available2019-03-28T13:53:04Z
dc.date.created2018-06-06T14:14:12Z
dc.date.issued2018
dc.identifier.citationJournal of the American Statistical Association. 2018, .nb_NO
dc.identifier.issn0162-1459
dc.identifier.urihttp://hdl.handle.net/11250/2592268
dc.description.abstractPriors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides limited information about the covariance structure under in-fill asymptotics. We extend the recent penalized complexity prior framework and develop a principled joint prior for the range and the marginal variance of one-dimensional, two-dimensional, and three-dimensional Matérn GRFs with fixed smoothness. The prior is weakly informative and penalizes complexity by shrinking the range toward infinity and the marginal variance toward zero. We propose guidelines for selecting the hyperparameters, and a simulation study shows that the new prior provides a principled alternative to reference priors that can leverage prior knowledge to achieve shorter credible intervals while maintaining good coverage. We extend the prior to a nonstationary GRF parameterized through local ranges and marginal standard deviations, and introduce a scheme for selecting the hyperparameters based on the coverage of the parameters when fitting simulated stationary data. The approach is applied to a dataset of annual precipitation in southern Norway and the scheme for selecting the hyperparameters leads to conservative estimates of nonstationarity and improved predictive performance over the stationary model. Supplementary materials for this article are available online.nb_NO
dc.language.isoengnb_NO
dc.publisherTaylor & Francisnb_NO
dc.titleConstructing Priors that Penalize the Complexity of Gaussian Random Fieldsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber8nb_NO
dc.source.journalJournal of the American Statistical Associationnb_NO
dc.identifier.doi10.1080/01621459.2017.1415907
dc.identifier.cristin1589489
dc.relation.projectNorges forskningsråd: 240873nb_NO
dc.description.localcodeLocked until 9.7.2019 due to copyright restrictions. This is an [Accepted Manuscript] of an article published by Taylor & Francis in [Journal of the American Statistical Association] on [09 Jul 2018], available at https://doi.org/10.1080/01621459.2017.1415907nb_NO
cristin.unitcode194,63,15,0
cristin.unitnameInstitutt for matematiske fag
cristin.ispublishedfalse
cristin.fulltextpostprint
cristin.qualitycode2


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