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dc.contributor.authorFuglstad, Geir-Arne
dc.contributor.authorHem, Ingeborg Gullikstad
dc.contributor.authorKnight, Alexander
dc.contributor.authorRue, Håvard
dc.contributor.authorRiebler, Andrea Ingeborg
dc.date.accessioned2019-12-02T14:03:17Z
dc.date.available2019-12-02T14:03:17Z
dc.date.created2019-10-11T13:41:56Z
dc.date.issued2019
dc.identifier.issn1936-0975
dc.identifier.urihttp://hdl.handle.net/11250/2631300
dc.description.abstractVariance parameters in additive models are typically assigned independent priors that do not account for model structure. We present a new framework for prior selection based on a hierarchical decomposition of the total variance along a tree structure to the individual model components. For each split in the tree, an analyst may be ignorant or have a sound intuition on how to attribute variance to the branches. In the former case a Dirichlet prior is appropriate to use, while in the latter case a penalised complexity (PC) prior provides robust shrinkage. A bottom-up combination of the conditional priors results in a proper joint prior. We suggest default values for the hyperparameters and offer intuitive statements for eliciting the hyperparameters based on expert knowledge. The prior framework is applicable for R packages for Bayesian inference such as INLA and RStan. Three simulation studies show that, in terms of the application-specific measures of interest, PC priors improve inference over Dirichlet priors when used to penalise different levels of complexity in splits. However, when expressing ignorance in a split, Dirichlet priors perform equally well and are preferred for their simplicity. We find that assigning current state-of-the-art default priors for each variance parameter individually is less transparent and does not perform better than using the proposed joint priors. We demonstrate practical use of the new framework by analysing spatial heterogeneity in neonatal mortality in Kenya in 2010–2014 based on complex survey data.nb_NO
dc.language.isoengnb_NO
dc.publisherInternational Society for Bayesian Analysis (ISBA)nb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleIntuitive Joint Priors for Variance Parametersnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.journalBayesian Analysisnb_NO
dc.identifier.doi10.1214/19-BA1185
dc.identifier.cristin1736431
dc.relation.projectNorges forskningsråd: 240873nb_NO
dc.description.localcode(C) 2019 International Society for Bayesian Analysis. Creative Commons Attribution 4.0 International License.nb_NO
cristin.unitcode194,63,15,0
cristin.unitnameInstitutt for matematiske fag
cristin.ispublishedfalse
cristin.fulltextoriginal
cristin.qualitycode1


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