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Article: Multi-parameter automodels and their applications

TitleMulti-parameter automodels and their applications
Authors
KeywordsAutomodel
Beta conditional
Multi-parameter exponential family
Spatial cooperation
Issue Date2008
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 2008, v. 95 n. 2, p. 335-349 How to Cite?
AbstractMotivated by the modelling of non-Gaussian data or positively correlated data on a lattice, extensions of Besag's automodels to exponential families with multi-dimensional parameters have been proposed recently. We provide a multiple-parameter analogue of Besag's one-dimensional result that gives the necessary form of the exponential families for the Markov random field's conditional distributions. We propose estimation of parameters by maximum pseudolikelihood and give a proof of the consistency of the estimators for the multi-parameter automodel. The methodology is illustrated with examples, in particular the building of a cooperative system with beta conditional distributions. We also indicate future applications of these models to the analysis of mixed-state spatial data. © 2008 Biometrika Trust.
Persistent Identifierhttp://hdl.handle.net/10722/132610
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorHardouin, Cen_HK
dc.contributor.authorYao, JFen_HK
dc.date.accessioned2011-03-28T09:26:59Z-
dc.date.available2011-03-28T09:26:59Z-
dc.date.issued2008en_HK
dc.identifier.citationBiometrika, 2008, v. 95 n. 2, p. 335-349en_HK
dc.identifier.issn0006-3444en_HK
dc.identifier.urihttp://hdl.handle.net/10722/132610-
dc.description.abstractMotivated by the modelling of non-Gaussian data or positively correlated data on a lattice, extensions of Besag's automodels to exponential families with multi-dimensional parameters have been proposed recently. We provide a multiple-parameter analogue of Besag's one-dimensional result that gives the necessary form of the exponential families for the Markov random field's conditional distributions. We propose estimation of parameters by maximum pseudolikelihood and give a proof of the consistency of the estimators for the multi-parameter automodel. The methodology is illustrated with examples, in particular the building of a cooperative system with beta conditional distributions. We also indicate future applications of these models to the analysis of mixed-state spatial data. © 2008 Biometrika Trust.en_HK
dc.languageengen_US
dc.publisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/en_HK
dc.relation.ispartofBiometrikaen_HK
dc.subjectAutomodelen_HK
dc.subjectBeta conditionalen_HK
dc.subjectMulti-parameter exponential familyen_HK
dc.subjectSpatial cooperationen_HK
dc.titleMulti-parameter automodels and their applicationsen_HK
dc.typeArticleen_HK
dc.identifier.emailYao, JF: jeffyao@hku.hken_HK
dc.identifier.authorityYao, JF=rp01473en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1093/biomet/asn016en_HK
dc.identifier.scopuseid_2-s2.0-44849133550en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-44849133550&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume95en_HK
dc.identifier.issue2en_HK
dc.identifier.spage335en_HK
dc.identifier.epage349en_HK
dc.identifier.eissn1464-3510-
dc.identifier.isiWOS:000256269100006-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridHardouin, C=15032906000en_HK
dc.identifier.scopusauthoridYao, JF=7403503451en_HK

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