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Universidade de Brasília
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dc.contributor.authorPrataviera, Fábio-
dc.contributor.authorVila Gabriel, Roberto-
dc.contributor.authorCancho, Vicente G.-
dc.contributor.authorOrtega, Edwin M. M.-
dc.contributor.authorCordeiro, Gauss M.-
dc.date.accessioned2022-05-03T15:18:03Z-
dc.date.available2022-05-03T15:18:03Z-
dc.date.issued2022-02-24-
dc.identifier.citationPRATAVIERA, Fábio et al. Reparameterized extended Maxwell regression: properties, estimation and application. Communications in Statistics - Theory and Methods, 2022. DOI: https://doi.org/10.1080/03610926.2022.2042561.pt_BR
dc.identifier.urihttps://repositorio.unb.br/handle/10482/43604-
dc.language.isoInglêspt_BR
dc.publisherTaylor & Francispt_BR
dc.rightsAcesso Restritopt_BR
dc.titleReparameterized extended Maxwell regression: Properties, estimation and applicationpt_BR
dc.typeArtigopt_BR
dc.subject.keywordInferência bayesianapt_BR
dc.subject.keywordDados experimentaispt_BR
dc.subject.keywordinferência de probabilidadept_BR
dc.subject.keywordMedianapt_BR
dc.subject.keywordModelo de regressãopt_BR
dc.identifier.doihttps://doi.org/10.1080/03610926.2022.2042561pt_BR
dc.relation.publisherversionhttps://www.tandfonline.com/doi/abs/10.1080/03610926.2022.2042561pt_BR
dc.description.abstract1We propose a reparameterized regression for the median of an extended Maxwell distribution that can be used when the response variable has a positive support. We obtain some structural properties of the distribution. The parameter estimates are obtained by maximum likelihood and Bayesian methods. Some influence measures and quantile residuals are defined. Several Monte Carlo simulations are reported for inference purposes. The new regression is applied to an experimental data set.pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0001-8190-1086pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0003-1073-0114pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0003-3999-7402pt_BR
dc.identifier.orcidhttps://orcid.org/ 0000-0002-3052-6551pt_BR
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