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Article: Higher-order Markov chain models for categorical data sequences

TitleHigher-order Markov chain models for categorical data sequences
Authors
Ching, WKFung, ESNg, MK
KeywordsCategorical data
Higher-order Markov model
Linear programming
Issue Date2004
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://as.wiley.com/WileyCDA/WileyTitle/productCd-NAV.html
Citation
Naval Research Logistics, 2004, v. 51 n. 4, p. 557-574 How to Cite?
DOI: http://dx.doi.org/10.1002/nav.20017
AbstractIn this paper we study higher-order Markov chain models for analyzing categorical data sequences. We propose an efficient estimation method for the model parameters. Data sequences such as DNA and sales demand are used to illustrate the predicting power of our proposed models. In particular, we apply the developed higher-order Markov chain model to the server logs data. The objective here is to model the users' behavior in accessing information and to predict their behavior in the future. Our tests are based on a realistic web log and our model shows an improvement in prediction. © 2004 Wiley Periodicals, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/75475
ISSN
0894-069X
2023 Impact Factor: 1.9
2023 SCImago Journal Rankings: 1.260
ISI Accession Number ID
WOS:000220912900007
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_HK
dc.contributor.authorFung, ESen_HK
dc.contributor.authorNg, MKen_HK
dc.date.accessioned2010-09-06T07:11:27Z-
dc.date.available2010-09-06T07:11:27Z-
dc.date.issued2004en_HK
dc.identifier.citationNaval Research Logistics, 2004, v. 51 n. 4, p. 557-574en_HK
dc.identifier.issn0894-069Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/75475-
dc.description.abstractIn this paper we study higher-order Markov chain models for analyzing categorical data sequences. We propose an efficient estimation method for the model parameters. Data sequences such as DNA and sales demand are used to illustrate the predicting power of our proposed models. In particular, we apply the developed higher-order Markov chain model to the server logs data. The objective here is to model the users' behavior in accessing information and to predict their behavior in the future. Our tests are based on a realistic web log and our model shows an improvement in prediction. © 2004 Wiley Periodicals, Inc.en_HK
dc.languageengen_HK
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://as.wiley.com/WileyCDA/WileyTitle/productCd-NAV.htmlen_HK
dc.relation.ispartofNaval Research Logisticsen_HK
dc.subjectCategorical dataen_HK
dc.subjectHigher-order Markov modelen_HK
dc.subjectLinear programmingen_HK
dc.titleHigher-order Markov chain models for categorical data sequencesen_HK
dc.typeArticleen_HK
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/nav.20017en_HK
dc.identifier.scopuseid_2-s2.0-2342466776en_HK
dc.identifier.hkuros88735en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-2342466776&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume51en_HK
dc.identifier.issue4en_HK
dc.identifier.spage557en_HK
dc.identifier.epage574en_HK
dc.identifier.isiWOS:000220912900007-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridFung, ES=7005440799en_HK
dc.identifier.scopusauthoridNg, MK=34571761900en_HK
dc.identifier.issnl0894-069X-

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