Bayesian varying coefficient models using PC priors
dc.contributor.author | Franco-Villoria, Maria | |
dc.contributor.author | Ventrucci, Massimo | |
dc.contributor.author | Rue, Haavard | |
dc.date.accessioned | 2019-04-28T13:15:06Z | |
dc.date.available | 2019-04-28T13:15:06Z | |
dc.date.issued | 2018-06-06 | |
dc.identifier.uri | http://hdl.handle.net/10754/632537 | |
dc.description.abstract | Varying 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.publisher | arXiv | |
dc.relation.url | http://arxiv.org/abs/1806.02084v1 | |
dc.relation.url | http://arxiv.org/pdf/1806.02084v1 | |
dc.rights | Archived with thanks to arXiv | |
dc.title | Bayesian varying coefficient models using PC priors | |
dc.type | Preprint | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | Statistics Program | |
dc.eprint.version | Pre-print | |
dc.contributor.institution | Department of Economics and Statistics,University of Torino | |
dc.contributor.institution | Department of Statistical Sciences,University of Bologna | |
dc.identifier.arxivid | 1806.02084 | |
kaust.person | Rue, Haavard | |
refterms.dateFOA | 2019-04-29T06:18:23Z |
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