Bayesian analysis for penalized spline regression using WinBUGS

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dc.contributor.author Crainiceanu, Ciprian en_US
dc.contributor.author Ruppert, David en_US
dc.contributor.author Wand, Matt en_US
dc.contributor.editor en_US
dc.date.accessioned 2011-02-07T06:17:52Z
dc.date.available 2011-02-07T06:17:52Z
dc.date.issued 2005 en_US
dc.identifier 2010000144 en_US
dc.identifier.citation Crainiceanu Ciprian, Ruppert David, and Wand Matt 2005, 'Bayesian analysis for penalized spline regression using WinBUGS', American Statistical Association, vol. 14, no. 14, en_US
dc.identifier.issn 1548-7660 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/12995
dc.description.abstract Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks. en_US
dc.language en_US
dc.publisher American Statistical Association en_US
dc.title Bayesian analysis for penalized spline regression using WinBUGS en_US
dc.parent Journal of Statistical Software en_US
dc.journal.volume 14 en_US
dc.journal.number 14 en_US
dc.publocation United States en_US
dc.identifier.startpage en_US
dc.identifier.endpage en_US
dc.cauo.name SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.for 010400 en_US
dc.personcode 0000064918 en_US
dc.personcode 0000064919 en_US
dc.personcode 110509 en_US
dc.percentage 100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords MCMC, semiparametric regression. en_US
dc.staffid 110509 en_US


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