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Article: On a mixture autoregressive model

TitleOn a mixture autoregressive model
Authors
KeywordsAutocorrelation
EM algorithm
Mixture autoregressive model
Model selection
Stationarity
Issue Date2000
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSB
Citation
Journal Of The Royal Statistical Society. Series B: Statistical Methodology, 2000, v. 62 n. 1, p. 95-115 How to Cite?
AbstractWe generalize the Gaussian mixture transition distribution (GMTD) model introduced by Le and co-workers to the mixture autoregressive (MAR) model for the modelling of non-linear time series. The models consist of a mixture of K stationary or non-stationary AR components. The advantages of the MAR model over the GMTD model include a more full range of shape changing predictive distributions and the ability to handle cycles and conditional heteroscedasticity in the time series. The stationarity conditions and autocorrelation function are derived. The estimation is easily done via a simple EM algorithm and the model selection problem is addressed. The shape changing feature of the conditional distributions makes these models capable of modelling time series with multimodal conditional distributions and with heteroscedasticity. The models are applied to two real data sets and compared with other competing models. The MAR models appear to capture features of the data better than other competing models do.
Persistent Identifierhttp://hdl.handle.net/10722/82800
ISSN
2015 Impact Factor: 4.222
2015 SCImago Journal Rankings: 7.429
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWong, CSen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-09-06T08:33:34Z-
dc.date.available2010-09-06T08:33:34Z-
dc.date.issued2000en_HK
dc.identifier.citationJournal Of The Royal Statistical Society. Series B: Statistical Methodology, 2000, v. 62 n. 1, p. 95-115en_HK
dc.identifier.issn1369-7412en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82800-
dc.description.abstractWe generalize the Gaussian mixture transition distribution (GMTD) model introduced by Le and co-workers to the mixture autoregressive (MAR) model for the modelling of non-linear time series. The models consist of a mixture of K stationary or non-stationary AR components. The advantages of the MAR model over the GMTD model include a more full range of shape changing predictive distributions and the ability to handle cycles and conditional heteroscedasticity in the time series. The stationarity conditions and autocorrelation function are derived. The estimation is easily done via a simple EM algorithm and the model selection problem is addressed. The shape changing feature of the conditional distributions makes these models capable of modelling time series with multimodal conditional distributions and with heteroscedasticity. The models are applied to two real data sets and compared with other competing models. The MAR models appear to capture features of the data better than other competing models do.en_HK
dc.languageengen_HK
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSBen_HK
dc.relation.ispartofJournal of the Royal Statistical Society. Series B: Statistical Methodologyen_HK
dc.subjectAutocorrelationen_HK
dc.subjectEM algorithmen_HK
dc.subjectMixture autoregressive modelen_HK
dc.subjectModel selectionen_HK
dc.subjectStationarityen_HK
dc.titleOn a mixture autoregressive modelen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0964-1998&volume=62&issue=1&spage=95&epage=115&date=2000&atitle=On+a+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.scopuseid_2-s2.0-0034358434en_HK
dc.identifier.hkuros47760en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034358434&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume62en_HK
dc.identifier.issue1en_HK
dc.identifier.spage95en_HK
dc.identifier.epage115en_HK
dc.identifier.isiWOS:000085327600007-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridWong, CS=20236705600en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK

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