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Article: On a logistic mixture autoregressive model

TitleOn a logistic mixture autoregressive model
Authors
KeywordsEM algorithm
Forecasting
Mixture model
Model selection
Issue Date2001
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 2001, v. 88 n. 3, p. 833-846 How to Cite?
AbstractWe generalise the mixture autoregressive, MAR, model to the logistic mixture autoregressive with exogenous variables, LMARX, model for the modelling of nonlinear time series. The models consist of a mixture of two Gaussian transfer function models with the mixing proportions changing over time. The model can also be considered as a generalisation of the self-exciting threshold autoregressive, SETAR, model and the open-loop threshold autoregressive, TARSO, model. The advantages of the LMARX model over other nonlinear time series models include a wider range of shape-changing predictive distributions, the ability to handle cycles and conditional heteroscedasticity in the time series and better point prediction. Estimation is easily done via a simple EM algorithm and the model selection problem is addressed. The models are applied to two real datasets and compared with other competing models. © 2001 Biometrika Trust.
Persistent Identifierhttp://hdl.handle.net/10722/83063
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWong, CSen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-09-06T08:36:29Z-
dc.date.available2010-09-06T08:36:29Z-
dc.date.issued2001en_HK
dc.identifier.citationBiometrika, 2001, v. 88 n. 3, p. 833-846en_HK
dc.identifier.issn0006-3444en_HK
dc.identifier.urihttp://hdl.handle.net/10722/83063-
dc.description.abstractWe generalise the mixture autoregressive, MAR, model to the logistic mixture autoregressive with exogenous variables, LMARX, model for the modelling of nonlinear time series. The models consist of a mixture of two Gaussian transfer function models with the mixing proportions changing over time. The model can also be considered as a generalisation of the self-exciting threshold autoregressive, SETAR, model and the open-loop threshold autoregressive, TARSO, model. The advantages of the LMARX model over other nonlinear time series models include a wider range of shape-changing predictive distributions, the ability to handle cycles and conditional heteroscedasticity in the time series and better point prediction. Estimation is easily done via a simple EM algorithm and the model selection problem is addressed. The models are applied to two real datasets and compared with other competing models. © 2001 Biometrika Trust.en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/en_HK
dc.relation.ispartofBiometrikaen_HK
dc.rightsBiometrika. Copyright © Oxford University Press.en_HK
dc.subjectEM algorithmen_HK
dc.subjectForecastingen_HK
dc.subjectMixture modelen_HK
dc.subjectModel selectionen_HK
dc.titleOn a logistic mixture autoregressive modelen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-3444&volume=88&issue=3&spage=833&epage=846&date=2001&atitle=On+a+logistic+mixture+autoregressive+modelen_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/biomet/88.3.833-
dc.identifier.scopuseid_2-s2.0-0005884541en_HK
dc.identifier.hkuros64618en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0005884541&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume88en_HK
dc.identifier.issue3en_HK
dc.identifier.spage833en_HK
dc.identifier.epage846en_HK
dc.identifier.isiWOS:000171536100017-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridWong, CS=36862846900en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK

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