Comparison between highly complex location models and GAMLSS

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorRamires, Thiago G.-
Autor(es): dc.creatorNakamura, Luiz R.-
Autor(es): dc.creatorRighetto, Ana J.-
Autor(es): dc.creatorCarvalho, Renan J.-
Autor(es): dc.creatorVieira, Lucas A.-
Autor(es): dc.creatorPereira, Carlos A. B.-
Data de aceite: dc.date.accessioned2026-02-09T12:44:04Z-
Data de disponibilização: dc.date.available2026-02-09T12:44:04Z-
Data de envio: dc.date.issued2022-07-21-
Data de envio: dc.date.issued2022-07-21-
Data de envio: dc.date.issued2020-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/50679-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1167214-
Descrição: dc.descriptionThis paper presents a discussion regarding regression models, especially those belonging to the location class. Our main motivation is that, with simple distributions having simple interpretations, in some cases, one gets better results than the ones obtained with overly complex distributions. For instance, with the reverse Gumbel (RG) distribution, it is possible to explain response variables by making use of the generalized additive models for location, scale, and shape (GAMLSS) framework, which allows the fitting of several parameters (characteristics) of the probabilistic distributions, like mean, mode, variance, and others. Three real data applications are used to compare several location models against the RG under the GAMLSS framework. The intention is to show that the use of a simple distribution (e.g., RG) based on a more sophisticated regression structure may be preferable than using a more complex location model.-
Idioma: dc.languageen-
Publicador: dc.publisherMDPI-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceEntropy-
Palavras-chave: dc.subjectBeyond mean regression-
Palavras-chave: dc.subjectDistributional regression-
Palavras-chave: dc.subjectParsimony principle-
Palavras-chave: dc.subjectRegression models-
Palavras-chave: dc.subjectSmoothing functions-
Palavras-chave: dc.subjectAlém da regressão média-
Palavras-chave: dc.subjectRegressão distributiva-
Palavras-chave: dc.subjectPrincípio da parcimônia-
Palavras-chave: dc.subjectFunções de suavização-
Título: dc.titleComparison between highly complex location models and GAMLSS-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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