Non-standard semiparametric regression via BRugs

Marley, JK; Wand, MP

Non-standard semiparametric regression via BRugs

Marley, JK Wand, MP

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Publication Type:: Journal Article
Citation:: Journal of Statistical Software, 2010, 37 (5), pp. 1 - 30
Issue Date:: 2010-01-01

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Field	Value	Language
dc.contributor.author	Marley, JK	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2010-01-01	en_US
dc.identifier.citation	Journal of Statistical Software, 2010, 37 (5), pp. 1 - 30	en_US
dc.identifier.issn	1548-7660	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12985
dc.description.abstract	We provide several illustrations of Bayesian semiparametric regression analyses in the BRugs package. BRugs facilitates use of the BUGS inference engine from the R computing environment and allows analyses to be managed using scripts. The examples are chosen to represent an array of non-standard situations, for which mixed model software is not viable. The situations include: the response variable being outside of the one-parameter exponential family, data subject to missingness, data subject to measurement error and parameters entering the model via an index.	en_US
dc.relation.ispartof	Journal of Statistical Software	en_US
dc.relation.isbasedon	10.18637/jss.v037.i05	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Non-standard semiparametric regression via BRugs	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	37	en_US
utslib.for	0104 Statistics	en_US
dc.location.activity	Waterloo, Canada
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.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	37	en_US

Abstract:

We provide several illustrations of Bayesian semiparametric regression analyses in the BRugs package. BRugs facilitates use of the BUGS inference engine from the R computing environment and allows analyses to be managed using scripts. The examples are chosen to represent an array of non-standard situations, for which mixed model software is not viable. The situations include: the response variable being outside of the one-parameter exponential family, data subject to missingness, data subject to measurement error and parameters entering the model via an index.

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http://hdl.handle.net/10453/12985