The Gamma-count distribution in the analysis of experimental underdispersed data

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorZeviani, Walmes Marques-
Autor(es): dc.creatorRibeiro Junior, Paulo Justiniano-
Autor(es): dc.creatorBonat, Wagner Hugo-
Autor(es): dc.creatorShimakura, Silvia Emiko-
Autor(es): dc.creatorMuniz, Joel Augusto-
Data de aceite: dc.date.accessioned2026-02-09T11:38:03Z-
Data de disponibilização: dc.date.available2026-02-09T11:38:03Z-
Data de envio: dc.date.issued2019-12-11-
Data de envio: dc.date.issued2019-12-11-
Data de envio: dc.date.issued2014-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/38208-
Fonte completa do material: dc.identifierhttps://www.tandfonline.com/doi/full/10.1080/02664763.2014.922168-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1143989-
Descrição: dc.descriptionEvent counts are response variables with non-negative integer values representing the number of times that an event occurs within a fixed domain such as a time interval, a geographical area or a cell of a contingency table. Analysis of counts by Gaussian regression models ignores the discreteness, asymmetry and heteroscedasticity and is inefficient, providing unrealistic standard errors or possibly negative predictions of the expected number of events. The Poisson regression is the standard model for count data with underlying assumptions on the generating process which may be implausible in many applications. Statisticians have long recognized the limitation of imposing equidispersion under the Poisson regression model. A typical situation is when the conditional variance exceeds the conditional mean, in which case models allowing for overdispersion are routinely used. Less reported is the case of underdispersion with fewer modeling alternatives and assessments available in the literature. One of such alternatives, the Gamma-count model, is adopted here in the analysis of an agronomic experiment designed to investigate the effect of levels of defoliation on different phenological states upon the number of cotton bolls. Data set and code for analysis are available as online supplements. Results show improvements over the Poisson model and the semi-parametric quasi-Poisson model in capturing the observed variability in the data. Estimating rather than assuming the underlying variance process leads to important insights into the process.-
Idioma: dc.languageen-
Publicador: dc.publisherTaylor & Francis-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceJournal of Applied Statistics-
Palavras-chave: dc.subjectPoisson regression-
Palavras-chave: dc.subjectLikelihood inference-
Palavras-chave: dc.subjectGamma-count-
Palavras-chave: dc.subjectRegressão de Poisson-
Palavras-chave: dc.subjectInferência de probabilidade-
Palavras-chave: dc.subjectContagem gama-
Título: dc.titleThe Gamma-count distribution in the analysis of experimental underdispersed data-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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