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dc.contributor.authorFranco-Villoria, Maria
dc.contributor.authorVentrucci, Massimo
dc.contributor.authorRue, Haavard
dc.date.accessioned2019-04-28T13:15:06Z
dc.date.available2019-04-28T13:15:06Z
dc.date.issued2018-06-06
dc.identifier.urihttp://hdl.handle.net/10754/632537
dc.description.abstractVarying coefficient models arise naturally as a flexible extension of a simpler model where the effect of the covariate is constant. In this work, we present varying coefficient models in a unified way using the recently proposed framework of penalized complexity (PC) priors to build priors that allow proper shrinkage to the simpler model, avoiding overfitting. We illustrate their application in two spatial examples where varying coefficient models are relevant.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1806.02084v1
dc.relation.urlhttp://arxiv.org/pdf/1806.02084v1
dc.rightsArchived with thanks to arXiv
dc.titleBayesian varying coefficient models using PC priors
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Economics and Statistics,University of Torino
dc.contributor.institutionDepartment of Statistical Sciences,University of Bologna
dc.identifier.arxivid1806.02084
kaust.personRue, Haavard
refterms.dateFOA2019-04-29T06:18:23Z


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