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dc.contributor.authorBonat, Wagner H.
dc.contributor.authorJørgensen, Bent
dc.contributor.authorKokonendji, Célestin C.
dc.contributor.authorHinde, John
dc.contributor.authorDemétrio, Clarice G. B.
dc.date.accessioned2018-09-20T16:01:20Z
dc.date.available2018-09-20T16:01:20Z
dc.date.issued2017-08-30
dc.identifier.citationBonat, Wagner H. Jørgensen, Bent; Kokonendji, Célestin C.; Hinde, John; Demétrio, Clarice G. B. (2017). Extended poisson–tweedie: properties and regression models for count data. Statistical Modelling: An International Journal 18 (1), 24-49
dc.identifier.issn1471-082X,1477-0342
dc.identifier.urihttp://hdl.handle.net/10379/10475
dc.description.abstractWe propose a new class of discrete generalized linear models based on the class of Poisson-Tweedie factorial dispersion models with variance of the form mu + phi mu(p), where mu is the mean and phi and p are the dispersion and Tweedie power parameters, respectively. The models are fitted by using an estimating function approach obtained by combining the quasi-score and Pearson estimating functions for the estimation of the regression and dispersion parameters, respectively. This provides a flexible and efficient regression methodology for a comprehensive family of count models including Hermite, Neyman Type A, Polya-Aeppli, negative binomial and Poisson-inverse Gaussian. The estimating function approach allows us to extend the Poisson-Tweedie distributions to deal with underdispersed count data by allowing negative values for the dispersion parameter phi. Furthermore, the Poisson-Tweedie family can automatically adapt to highly skewed count data with excessive zeros, without the need to introduce zero-inflated or hurdle components, by the simple estimation of the power parameter. Thus, the proposed models offer a unified framework to deal with under-, equi-, overdispersed, zero-inflated and heavy-tailed count data. The computational implementation of the proposed models is fast, relying only on a simple Newton scoring algorithm. Simulation studies showed that the estimating function approach provides unbiased and consistent estimators for both regression and dispersion parameters. We highlight the ability of the Poisson-Tweedie distributions to deal with count data through a consideration of dispersion, zero-inflated and heavy tail indices, and illustrate its application with four data analyses. We provide an R implementation and the datasets as supplementary materials.
dc.publisherSAGE Publications
dc.relation.ispartofStatistical Modelling: An International Journal
dc.subjectcount data
dc.subjectestimating functions
dc.subjectoverdispersion
dc.subjectunderdispersion
dc.subjectpoisson-tweedie distribution
dc.subjectgeneralized linear-models
dc.subjectestimating equations
dc.subjectdispersion models
dc.subjectlongitudinal data
dc.subjectasymptotics
dc.subjectradiation
dc.subjectfamily
dc.titleExtended poisson–tweedie: properties and regression models for count data
dc.typeArticle
dc.identifier.doi10.1177/1471082x17715718
dc.local.publishedsourcehttp://arxiv.org/pdf/1608.06888
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