Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/24674
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dc.contributor.authorSantos, Cláudiapt_PT
dc.contributor.authorPereira, Isabelpt_PT
dc.contributor.authorScotto, M. G.pt_PT
dc.date.accessioned2018-11-19T13:45:19Z-
dc.date.available2018-11-19T13:45:19Z-
dc.date.issued2018-09-
dc.identifier.isbn978-84-17293-57-4-
dc.identifier.urihttp://hdl.handle.net/10773/24674-
dc.description.abstractA multivariate integer-valued autoregressive model of order one with periodic time-varying parameters, and driven by a periodic inno- vations sequence of independent random vectors is established. The bino- mial thinning operator replaces the scalar multiplication in the common time series models. The matricial form of the multivariate model and its basic statistical properties are de ned. Emphasis is placed upon models with periodic multivariate negative binomial innovations. Aiming to reduce computational burden arising from the use of the conditional maximum likelihood method a composite likelihood-based approach is adopted and compared with other traditional competitors.pt_PT
dc.language.isoengpt_PT
dc.publisherGodel Impresiones Digitales S.L.pt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147206/PTpt_PT
dc.rightsopenAccesspt_PT
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMultivariate models,,pt_PT
dc.subjectBinomial thinning operatorpt_PT
dc.subjectComposite likelihoodpt_PT
dc.titleMultivariate INAR processes – Periodic casept_PT
dc.typeconferenceObjectpt_PT
dc.description.versionpublishedpt_PT
dc.peerreviewedyespt_PT
ua.event.date19-21 September, 2018pt_PT
degois.publication.firstPage997pt_PT
degois.publication.lastPage1008pt_PT
degois.publication.locationGranadapt_PT
degois.publication.titleITISE 2018 International Conference on Time Series Analysis and Forecastingpt_PT
degois.publication.volume1pt_PT
dc.relation.publisherversionhttp://itise.ugr.espt_PT
Appears in Collections:CIDMA - Comunicações
PSG - Comunicações

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