An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion

dc.contributor.authorBourguignon, Marcelo
dc.contributor.authorWeiss, Christian H.
dc.date.accessioned2022-11-10T19:35:33Z
dc.date.available2022-11-10T19:35:33Z
dc.date.issued2017-04
dc.description.resumoWe present a novel first-order nonnegative integer-valued autoregressive model for stationary count data processes with Bernoulli-geometric marginals based on a new type of generalized thinning operator. It can be used for modeling time series of counts with equidispersion, underdispersion and overdispersion. The main properties of the model are derived, such as probability generating function, moments, transition probabilities and zero probability. The maximum likelihood method is used for estimating the model parameters. The proposed model is fitted to time series of counts of iceberg orders and of cases of family violence illustrating its capabilities in challenging cases of overdispersed and equidispersed count data.pt_BR
dc.identifier.citationBOURGUIGNON, Marcelo; WEISS, C. H. . An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion. Test, v. 26, p. 847-868, 2017. Disponível em: https://link.springer.com/article/10.1007%2Fs11749-017-0536-4. Acesso em: 07 dez. 2017pt_BR
dc.identifier.doi10.1007/s11749-017-0536-4
dc.identifier.urihttps://repositorio.ufrn.br/handle/123456789/49725
dc.languageenpt_BR
dc.publisherSpringerpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectINAR(1) processpt_BR
dc.subjectBernoulli distributionpt_BR
dc.subjectGeometric distributionpt_BR
dc.subjectInteger-valued time seriespt_BR
dc.subjectBinomial thinningpt_BR
dc.subjectNegative binomial thinningpt_BR
dc.titleAn INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersionpt_BR
dc.typearticlept_BR

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