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Article: On the autopersistence functions and the autopersistence graphs of binary autoregressive time series

TitleOn the autopersistence functions and the autopersistence graphs of binary autoregressive time series
Authors
KeywordsAsymptotic normality
Autopersistence function
Autopersistence graph
Binary time series
Consistency
Markov chain
Issue Date2011
PublisherBlackwell Publishing Ltd..
Citation
Journal Of Time Series Analysis, 2011, v. 32 n. 6, p. 639-646 How to Cite?
AbstractThe classical autocorrelation function may not be an effective and informative means in revealing the dependence features of a binary time series {y t}. Recently, the autopersistence functions defined as APF 0(k)=P(y t+k=1|y t=0) and APF 1(k)=P(y t+k=1|y t=1), k=1, 2,..., have been proposed as alternatives to the autocorrelation function for binary time series. In this article we consider the theoretical autopersistence functions and their natural sample analogues, the autopersistence graphs, under a binary autoregressive model framework. Some properties of the autopersistence functions and the asymptotic properties of the autopersistence graphs are discussed. The results have potential application in the modelling of binary time series. © 2011 Blackwell Publishing Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/143390
ISSN
2015 Impact Factor: 1.0
2015 SCImago Journal Rankings: 1.177
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong Research Grant CouncilHKU7029/08P
Hong Kong UGCSEG HKU09
Funding Information:

The authors thank an anonymous associate editor and an anonymous reviewer for the useful comments that have helped to improve the article considerably. The authors also acknowledge partial support from the Hong Kong Research Grant Council GRF grant (HKU7029/08P) and the Hong Kong UGC Special Equipment Grant (SEG HKU09).

References

 

DC FieldValueLanguage
dc.contributor.authorWang, Cen_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2011-11-24T10:04:57Z-
dc.date.available2011-11-24T10:04:57Z-
dc.date.issued2011en_HK
dc.identifier.citationJournal Of Time Series Analysis, 2011, v. 32 n. 6, p. 639-646en_HK
dc.identifier.issn0143-9782en_HK
dc.identifier.urihttp://hdl.handle.net/10722/143390-
dc.description.abstractThe classical autocorrelation function may not be an effective and informative means in revealing the dependence features of a binary time series {y t}. Recently, the autopersistence functions defined as APF 0(k)=P(y t+k=1|y t=0) and APF 1(k)=P(y t+k=1|y t=1), k=1, 2,..., have been proposed as alternatives to the autocorrelation function for binary time series. In this article we consider the theoretical autopersistence functions and their natural sample analogues, the autopersistence graphs, under a binary autoregressive model framework. Some properties of the autopersistence functions and the asymptotic properties of the autopersistence graphs are discussed. The results have potential application in the modelling of binary time series. © 2011 Blackwell Publishing Ltd.en_HK
dc.languageengen_US
dc.publisherBlackwell Publishing Ltd..en_US
dc.relation.ispartofJournal of Time Series Analysisen_HK
dc.subjectAsymptotic normalityen_HK
dc.subjectAutopersistence functionen_HK
dc.subjectAutopersistence graphen_HK
dc.subjectBinary time seriesen_HK
dc.subjectConsistencyen_HK
dc.subjectMarkov chainen_HK
dc.titleOn the autopersistence functions and the autopersistence graphs of binary autoregressive time seriesen_HK
dc.typeArticleen_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1467-9892.2011.00721.xen_HK
dc.identifier.scopuseid_2-s2.0-80053912417en_HK
dc.identifier.hkuros197786en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80053912417&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume32en_HK
dc.identifier.issue6en_HK
dc.identifier.spage639en_HK
dc.identifier.epage646en_HK
dc.identifier.isiWOS:000296263300006-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridWang, C=37065253600en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK

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