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Time inhomogeneous multivariate Markov chains

2024-03Artigo científico Acesso aberto
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dc.contributor.authorDamásio, Bruno
dc.contributor.authorNicolau, João
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblElsevier
dc.date.accessioned2024-01-24T17:11:51Z
dc.date.available2024-01-24T17:11:51Z
dc.date.issued2024-03
dc.descriptionDamásio, B., & Nicolau, J. (2024). Time inhomogeneous multivariate Markov chains: Detecting and testing multiple structural breaks occurring at unknown dates. Chaos, Solitons & Fractals, 180, 1-15. Article 114478. https://doi.org/10.1016/j.chaos.2024.114478 --- Bruno Damásio acknowledges the financial support provided by Fundação para a Ciência e a Tecnologia, Portugal (FCT) under the project UIDB/04152/2020 — Centro de Investigação em Gestão de Informação (MagIC). João Nicolau acknowledges the financial support provided by FCT under the project CEMAPRE/REM - UIDB/05069/2020.
dc.description.abstractMarkov chain models are used in several applications and different areas of study. A Markov chain model is usually assumed to be homogeneous in the sense that the transition probabilities are time-invariant. Yet, ignoring the inhomogeneous nature of a stochastic process by disregarding the presence of structural breaks can lead to misleading conclusions. Several methodologies are currently proposed for detecting structural breaks in a Markov chain. However, these methods have some limitations: namely they can only test directly for the presence of a single structural break. This paper proposes a new methodology for detecting and testing the presence of multiple structural breaks in a Markov chain occurring at unknown dates.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent15
dc.format.extent1243848
dc.identifier.doi10.1016/j.chaos.2024.114478
dc.identifier.issn0960-0779
dc.identifier.otherPURE: 82237761
dc.identifier.otherPURE UUID: 411e856d-06b7-49fc-b36b-ebc62d4da7e1
dc.identifier.otherScopus: 85183472738
dc.identifier.otherWOS: 001167025500001
dc.identifier.urihttp://hdl.handle.net/10362/162726
dc.identifier.urlhttps://www.scopus.com/pages/publications/85183472738
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001167025500001
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectInhomogeneous Markov chain
dc.subjectStructural breaks
dc.subjectTime-varying probabilities
dc.subjectStatistical and Nonlinear Physics
dc.subjectMathematical Physics
dc.subjectGeneral Physics and Astronomy
dc.subjectApplied Mathematics
dc.titleTime inhomogeneous multivariate Markov chainsen
dc.title.subtitleDetecting and testing multiple structural breaks occurring at unknown datesen
dc.typejournal article
degois.publication.firstPage1
degois.publication.lastPage15
degois.publication.titleChaos, Solitons & Fractals
degois.publication.volume180
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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