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Article: Mixtures of nonparametric autoregressions

TitleMixtures of nonparametric autoregressions
Authors
Franke, JStockis, JPTadjuidjeKamgaing, JLi, WK
KeywordsEM algorithm
Hidden variables
Kernel estimates
Local likelihood
Mixture
Nonparametric autoregression
Nonparametric regression
Issue Date2011
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/10485252.asp
Citation
Journal Of Nonparametric Statistics, 2011, v. 23 n. 2, p. 287-303 How to Cite?
DOI: http://dx.doi.org/10.1080/10485252.2010.539686
AbstractWe consider data generating mechanisms which can be represented as mixtures of finitely many regression or autoregression models.We propose nonparametric estimators for the functions characterising the various mixture components based on a local quasi maximum likelihood approach and prove their consistency. We present an EM algorithm for calculating the estimates numerically which is mainly based on iteratively applying common local smoothers and discuss its convergence properties. © American Statistical Association and Taylor & Francis 2011.
Persistent Identifierhttp://hdl.handle.net/10722/134474
ISSN
1048-5252
2023 Impact Factor: 0.8
2023 SCImago Journal Rankings: 0.440
ISI Accession Number ID
WOS:000290679100003
Funding AgencyGrant Number
Deutsche Forschungsgemeinschaft (DFG)
state of Rhineland-Palatinate
Funding Information:

We thank an anonymous referee for recommendations which led to a considerable improvement of the paper. The work was supported by the Deutsche Forschungsgemeinschaft (DFG) as well as by the Center for Mathematical and Computational Modelling (CM)2 funded by the state of Rhineland-Palatinate.

References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorFranke, Jen_HK
dc.contributor.authorStockis, JPen_HK
dc.contributor.authorTadjuidjeKamgaing, Jen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2011-06-17T09:21:30Z-
dc.date.available2011-06-17T09:21:30Z-
dc.date.issued2011en_HK
dc.identifier.citationJournal Of Nonparametric Statistics, 2011, v. 23 n. 2, p. 287-303en_HK
dc.identifier.issn1048-5252en_HK
dc.identifier.urihttp://hdl.handle.net/10722/134474-
dc.description.abstractWe consider data generating mechanisms which can be represented as mixtures of finitely many regression or autoregression models.We propose nonparametric estimators for the functions characterising the various mixture components based on a local quasi maximum likelihood approach and prove their consistency. We present an EM algorithm for calculating the estimates numerically which is mainly based on iteratively applying common local smoothers and discuss its convergence properties. © American Statistical Association and Taylor & Francis 2011.en_HK
dc.languageengen_US
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/10485252.aspen_HK
dc.relation.ispartofJournal of Nonparametric Statisticsen_HK
dc.rightsThis is an electronic version of an article published in Journal of Nonparametric Statistics, 2011, v. 23 n. 2, p. 287-303. The article is available online at: http://www.tandfonline.com/doi/abs/10.1080/10485252.2010.539686-
dc.subjectEM algorithmen_HK
dc.subjectHidden variablesen_HK
dc.subjectKernel estimatesen_HK
dc.subjectLocal likelihooden_HK
dc.subjectMixtureen_HK
dc.subjectNonparametric autoregressionen_HK
dc.subjectNonparametric regressionen_HK
dc.titleMixtures of nonparametric autoregressionsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1048-5252&volume=23&issue=2&spage=287&epage=303&date=2011&atitle=Mixtures+of+nonparametric+autoregressions-
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1080/10485252.2010.539686en_HK
dc.identifier.scopuseid_2-s2.0-79957586762en_HK
dc.identifier.hkuros185845en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79957586762&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume23en_HK
dc.identifier.issue2en_HK
dc.identifier.spage287en_HK
dc.identifier.epage303en_HK
dc.identifier.isiWOS:000290679100003-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridFranke, J=7201859718en_HK
dc.identifier.scopusauthoridStockis, JP=6506996975en_HK
dc.identifier.scopusauthoridTadjuidjeKamgaing, J=23977086500en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.citeulike9323398-
dc.identifier.issnl1026-7654-

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