The deviance information criterion in comparison of normal mixing models

Fung, T; Wang, JJJ; Seneta, E

The deviance information criterion in comparison of normal mixing models

Fung, T Wang, JJJ

Seneta, E

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Publication Type:: Journal Article
Citation:: International Statistical Review, 2014, 82 (3), pp. 411 - 421
Issue Date:: 2014-12-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Fung, T	en_US
dc.contributor.author	Wang, JJJ https://orcid.org/0000-0003-0088-7003	en_US
dc.contributor.author	Seneta, E	en_US
dc.date.issued	2014-12-01	en_US
dc.identifier.citation	International Statistical Review, 2014, 82 (3), pp. 411 - 421	en_US
dc.identifier.issn	0306-7734	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118798
dc.description.abstract	© 2014 International Statistical Institute. Model selection from several non-nested models by using the deviance information criterion within Bayesian inference Using Gibbs Sampling (BUGS) software needs to be treated with caution. This is particularly important if one can specify a model in various mixing representations, as for the normal variance-mean mixing distribution occurring in financial contexts. We propose a procedure to compare goodness of fit of several non-nested models, which uses BUGS software in part.	en_US
dc.relation.ispartof	International Statistical Review	en_US
dc.relation.isbasedon	10.1111/insr.12063	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	The deviance information criterion in comparison of normal mixing models	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	82	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0199 Other Mathematical Sciences	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.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	82	en_US

Abstract:

© 2014 International Statistical Institute. Model selection from several non-nested models by using the deviance information criterion within Bayesian inference Using Gibbs Sampling (BUGS) software needs to be treated with caution. This is particularly important if one can specify a model in various mixing representations, as for the normal variance-mean mixing distribution occurring in financial contexts. We propose a procedure to compare goodness of fit of several non-nested models, which uses BUGS software in part.

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http://hdl.handle.net/10453/118798