Bayesian analysis for penalized spline regression using WinBUGS

Crainiceanu, CM; Ruppert, D; Wand, MP

Bayesian analysis for penalized spline regression using WinBUGS

Crainiceanu, CM Ruppert, D Wand, MP

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Publication Type:: Journal Article
Citation:: Journal of Statistical Software, 2005, 14
Issue Date:: 2005-11-10

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Field	Value	Language
dc.contributor.author	Crainiceanu, CM	en_US
dc.contributor.author	Ruppert, D	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2005-11-10	en_US
dc.identifier.citation	Journal of Statistical Software, 2005, 14	en_US
dc.identifier.issn	1548-7660	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 Win BUGS. 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.relation.ispartof	Journal of Statistical Software	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Bayesian analysis for penalized spline regression using WinBUGS	en_US
dc.type	Journal Article
utslib.citation.volume	14	en_US
utslib.for	0104 Statistics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	14	en_US

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 Win BUGS. 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.

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http://hdl.handle.net/10453/12995