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Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42445
DC FieldValueLanguage
dc.contributor.authorGómez-Déniz, Emilioen_US
dc.contributor.authorGhitany, M. E.en_US
dc.contributor.authorGupta, Ramesh C.en_US
dc.date.accessioned2018-11-14T09:43:51Z-
dc.date.available2018-11-14T09:43:51Z-
dc.date.issued2016en_US
dc.identifier.issn0361-0918en_US
dc.identifier.urihttp://hdl.handle.net/10553/42445-
dc.description.abstractIn this article, we have developed a Poisson-mixed inverse Gaussian (PMIG) distribution. The mixed inverse Gaussian distribution is a mixture of the inverse Gaussian distribution and its length-biased counterpart. A PMIG regression model is developed and the maximum likelihood estimation of the parameters is studied. A dataset dealing with the number of hospital stays among the elderly population is analyzed by using the PMIG and the PIG (Poisson-inverse Gaussian) regression models and it has been shown that the PMIG model fits the data better than the PIG model.en_US
dc.languageengen_US
dc.relation.ispartofCommunications in Statistics Part B: Simulation and Computationen_US
dc.sourceCommunications in Statistics: Simulation and Computation[ISSN 0361-0918],v. 45, p. 2767-2781en_US
dc.subject12 Matemáticasen_US
dc.subject.otherAkaike information criterionen_US
dc.subject.otherMaximum likelihooden_US
dc.subject.otherMixture inverse Gaussian distributionen_US
dc.subject.otherOver-dispersionen_US
dc.subject.otherRegression analysis estimationen_US
dc.titlePoisson-mixed inverse Gaussian regression model and its applicationen_US
dc.typeinfo:eu-repo/semantics/Articlees
dc.typeArticlees
dc.identifier.doi10.1080/03610918.2014.925924
dc.identifier.scopus84976557477-
dc.identifier.isi000379593400009
dc.contributor.authorscopusid15724912000
dc.contributor.authorscopusid6602733980
dc.contributor.authorscopusid55705318800
dc.description.lastpage2781-
dc.identifier.issue8-
dc.description.firstpage2767-
dc.relation.volume45-
dc.investigacionCiencias Sociales y Jurídicasen_US
dc.type2Artículoen_US
dc.contributor.daisngid610603
dc.contributor.daisngid977733
dc.contributor.daisngid218753
dc.contributor.wosstandardWOS:Gomez-Deniz, E
dc.contributor.wosstandardWOS:Ghitany, ME
dc.contributor.wosstandardWOS:Gupta, RC
dc.date.coverdateSeptiembre 2016
dc.identifier.ulpgcSíes
dc.description.sjr0,578
dc.description.jcr0,457
dc.description.sjrqQ3
dc.description.jcrqQ4
dc.description.scieSCIE
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR TIDES- Técnicas estadísticas bayesianas y de decisión en la economía y empresa-
crisitem.author.deptIU de Turismo y Desarrollo Económico Sostenible-
crisitem.author.deptDepartamento de Métodos Cuantitativos en Economía y Gestión-
crisitem.author.orcid0000-0002-5072-7908-
crisitem.author.parentorgIU de Turismo y Desarrollo Económico Sostenible-
crisitem.author.fullNameGómez Déniz, Emilio-
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