Parsimonious classification via generalized linear mixed models

Kauermann, G; Ormerod, JT; Wand, MP

Parsimonious classification via generalized linear mixed models

Kauermann, G Ormerod, JT Wand, MP

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Publication Type:: Journal Article
Citation:: Journal of Classification, 2010, 27 (1), pp. 89 - 110
Issue Date:: 2010-03-01

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Field	Value	Language
dc.contributor.author	Kauermann, G	en_US
dc.contributor.author	Ormerod, JT	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2010-03-01	en_US
dc.identifier.citation	Journal of Classification, 2010, 27 (1), pp. 89 - 110	en_US
dc.identifier.issn	0176-4268	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14514
dc.description.abstract	We devise a classification algorithm based on generalized linear mixed model (GLMM) technology. The algorithm incorporates spline smoothing, additive model-type structures and model selection. For reasons of speed we employ the Laplace approximation, rather than Monte Carlo methods. Tests on real and simulated data show the algorithm to have good classification performance. Moreover, the resulting classifiers are generally interpretable and parsimonious. © 2010 Springer Science+Business Media, LLC.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0556518
dc.relation.ispartof	Journal of Classification	en_US
dc.relation.isbasedon	10.1007/s00357-010-9045-9	en_US
dc.subject.classification	Social Sciences Methods	en_US
dc.title	Parsimonious classification via generalized linear mixed models	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	27	en_US
utslib.for	0104 Statistics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	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	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	27	en_US

Abstract:

We devise a classification algorithm based on generalized linear mixed model (GLMM) technology. The algorithm incorporates spline smoothing, additive model-type structures and model selection. For reasons of speed we employ the Laplace approximation, rather than Monte Carlo methods. Tests on real and simulated data show the algorithm to have good classification performance. Moreover, the resulting classifiers are generally interpretable and parsimonious. © 2010 Springer Science+Business Media, LLC.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/14514