Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/29228
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dc.contributor.authorSilva, Isabelpt_PT
dc.contributor.authorSilva, Maria Eduardapt_PT
dc.contributor.authorTorres, Cristinapt_PT
dc.date.accessioned2020-09-15T11:56:19Z-
dc.date.issued2020-
dc.identifier.issn0266-4763pt_PT
dc.identifier.urihttp://hdl.handle.net/10773/29228-
dc.description.abstractTime series of (small) counts are common in practice and appear in a wide variety of fields. In the last three decades, several models that explicitly account for the discreteness of the data have been proposed in the literature. However, for multivariate time series of counts several difficulties arise and the literature is not so detailed. This work considers Bivariate INteger-valued Moving Average, BINMA, models based on the binomial thinning operation. The main probabilistic and statistical properties of BINMA models are studied. Two parametric cases are analysed, one with the cross-correlation generated through a Bivariate Poisson innovation process and another with a Bivariate Negative Binomial innovation process. Moreover, parameter estimation is carried out by the Generalized Method of Moments. The performance of the model is illustrated with synthetic data as well as with real datasets.pt_PT
dc.language.isoengpt_PT
dc.publisherTaylor & Francispt_PT
dc.relationUIDB/04106/2020pt_PT
dc.relationUIDP/04106/2020pt_PT
dc.rightsembargoedAccesspt_PT
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBivariate discrete distributionspt_PT
dc.subjectBivariate modelspt_PT
dc.subjectGeneralized method of momentspt_PT
dc.subjectMoving averagept_PT
dc.subjectTime series of countspt_PT
dc.titleInference for bivariate integer-valued moving average models based on binomial thinning operationpt_PT
dc.typearticlept_PT
dc.description.versionpublishedpt_PT
dc.peerreviewedyespt_PT
degois.publication.firstPage2546pt_PT
degois.publication.issue13-15pt_PT
degois.publication.lastPage2564pt_PT
degois.publication.titleJournal of Applied Statisticspt_PT
degois.publication.volume47pt_PT
dc.date.embargo2021-04-01-
dc.relation.publisherversionhttps://www.tandfonline.com/doi/full/10.1080/02664763.2020.1747411pt_PT
dc.identifier.doi10.1080/02664763.2020.1747411pt_PT
dc.identifier.essn1360-0532pt_PT
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PSG - Artigos

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