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dc.contributor.authorMartínez-Flórez, Guillermo
dc.contributor.authorBolfarine, Heleno
dc.contributor.authorGómez, Héctor W.
dc.date.accessioned2016-07-05T15:23:16Z
dc.date.available2016-07-05T15:23:16Z
dc.date.issued2015-12
dc.identifier.citationMartínez-Flórez, Guillermo; Bolfarine, Heleno; Gómez, Héctor W. Likelihood-based inference for the power regression model. "SORT", Desembre 2015, vol. 39, núm. 2, p. 187-208.
dc.identifier.issn1696-2281
dc.identifier.urihttp://hdl.handle.net/2117/88527
dc.description.abstractIn this paper we investigate an extension of the power-normal model, called the alpha-power model and specialize it to linear and nonlinear regression models, with and without correlated errors. Maximum likelihood estimation is considered with explicit derivation of the observed and expected Fisher information matrices. Applications are considered for the Australian athletes data set and also to a data set studied in Xie et al. (2009). The main conclusion is that the proposed model can be a viable alternative in situations were the normal distribution is not the most adequate model.
dc.format.extent22 p.
dc.language.isoeng
dc.publisherInstitut d'Estadística de Catalunya
dc.relation.ispartofSORT. 2015, Vol. 39, Núm. 2
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.otherCorrelation
dc.subject.othermaximum likelihood
dc.subject.otherpower-normal distribution
dc.subject.otherregression.
dc.titleLikelihood-based inference for the power regression model
dc.typeArticle
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::60 Probability theory and stochastic processes::60E Distribution theory
dc.rights.accessOpen Access
upcommons.citation.publishedtrue
upcommons.citation.publicationNameSORT
upcommons.citation.volume39
upcommons.citation.number2
upcommons.citation.startingPage187
upcommons.citation.endingPage208


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