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Article: Higher-order multivariate Markov chains and their applications

TitleHigher-order multivariate Markov chains and their applications
Authors
KeywordsCategorical data sequences
Multivariate Markov chains
Perron-Frobenius theorem
Issue Date2008
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/laa
Citation
Linear Algebra And Its Applications, 2008, v. 428 n. 2-3, p. 492-507 How to Cite?
AbstractMarkov chains are commonly used in modeling many practical systems such as queuing systems, manufacturing systems and inventory systems. They are also effective in modeling categorical data sequences. In a conventional nth order multivariate Markov chain model of s chains, and each chain has the same set of m states, the total number of parameters required to set up the model is O (m ns). Such huge number of states discourages researchers or practitioners from using them directly. In this paper, we propose an nth-order multivariate Markov chain model for modeling multiple categorical data sequences such that the total number of parameters are of O (ns 2 m 2). The proposed model requires significantly less parameters than the conventional one. We develop efficient estimation methods for the model parameters. An application to demand predictions in inventory control is also discussed. © 2007 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/75421
ISSN
2015 Impact Factor: 0.965
2015 SCImago Journal Rankings: 0.837
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_HK
dc.contributor.authorNg, MKen_HK
dc.contributor.authorFung, ESen_HK
dc.date.accessioned2010-09-06T07:10:57Z-
dc.date.available2010-09-06T07:10:57Z-
dc.date.issued2008en_HK
dc.identifier.citationLinear Algebra And Its Applications, 2008, v. 428 n. 2-3, p. 492-507en_HK
dc.identifier.issn0024-3795en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75421-
dc.description.abstractMarkov chains are commonly used in modeling many practical systems such as queuing systems, manufacturing systems and inventory systems. They are also effective in modeling categorical data sequences. In a conventional nth order multivariate Markov chain model of s chains, and each chain has the same set of m states, the total number of parameters required to set up the model is O (m ns). Such huge number of states discourages researchers or practitioners from using them directly. In this paper, we propose an nth-order multivariate Markov chain model for modeling multiple categorical data sequences such that the total number of parameters are of O (ns 2 m 2). The proposed model requires significantly less parameters than the conventional one. We develop efficient estimation methods for the model parameters. An application to demand predictions in inventory control is also discussed. © 2007 Elsevier Inc. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/laaen_HK
dc.relation.ispartofLinear Algebra and Its Applicationsen_HK
dc.rightsLinear Algebra and Its Applications. Copyright © Elsevier Inc.en_HK
dc.subjectCategorical data sequencesen_HK
dc.subjectMultivariate Markov chainsen_HK
dc.subjectPerron-Frobenius theoremen_HK
dc.titleHigher-order multivariate Markov chains and their applicationsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0024-3795&volume=428&spage=492&epage=507&date=2008&atitle=Higher-order+Multivariate+Markov+Chains+and+their+Applicationsen_HK
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.laa.2007.05.021en_HK
dc.identifier.scopuseid_2-s2.0-36048957576en_HK
dc.identifier.hkuros141910en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-36048957576&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume428en_HK
dc.identifier.issue2-3en_HK
dc.identifier.spage492en_HK
dc.identifier.epage507en_HK
dc.identifier.isiWOS:000252172800008-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridNg, MK=34571761900en_HK
dc.identifier.scopusauthoridFung, ES=7005440799en_HK

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