Likelihood-based inference for the power regression model

In 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...

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Detalles Bibliográficos
Autores: Martínez-Flórez, Guillermo, Bolfarine, Heleno, Gómez, Héctor W.
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/88527
Acceso en línea:https://hdl.handle.net/2117/88527
Access Level:acceso abierto
Palabra clave:Correlation
maximum likelihood
power-normal distribution
regression.
Classificació AMS::60 Probability theory and stochastic processes::60E Distribution theory
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
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spelling Likelihood-based inference for the power regression modelMartínez-Flórez, GuillermoBolfarine, HelenoGómez, Héctor W.Correlationmaximum likelihoodpower-normal distributionregression.Classificació AMS::60 Probability theory and stochastic processes::60E Distribution theoryÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàticaIn 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.Peer ReviewedInstitut d'Estadística de Catalunya20152015-12-0120162016-07-05journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/88527reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/885272026-05-27T15:37:01Z
dc.title.none.fl_str_mv Likelihood-based inference for the power regression model
title Likelihood-based inference for the power regression model
spellingShingle Likelihood-based inference for the power regression model
Martínez-Flórez, Guillermo
Correlation
maximum likelihood
power-normal distribution
regression.
Classificació AMS::60 Probability theory and stochastic processes::60E Distribution theory
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
title_short Likelihood-based inference for the power regression model
title_full Likelihood-based inference for the power regression model
title_fullStr Likelihood-based inference for the power regression model
title_full_unstemmed Likelihood-based inference for the power regression model
title_sort Likelihood-based inference for the power regression model
dc.creator.none.fl_str_mv Martínez-Flórez, Guillermo
Bolfarine, Heleno
Gómez, Héctor W.
author Martínez-Flórez, Guillermo
author_facet Martínez-Flórez, Guillermo
Bolfarine, Heleno
Gómez, Héctor W.
author_role author
author2 Bolfarine, Heleno
Gómez, Héctor W.
author2_role author
author
dc.subject.none.fl_str_mv Correlation
maximum likelihood
power-normal distribution
regression.
Classificació AMS::60 Probability theory and stochastic processes::60E Distribution theory
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
topic Correlation
maximum likelihood
power-normal distribution
regression.
Classificació AMS::60 Probability theory and stochastic processes::60E Distribution theory
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
description In 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.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-12-01
2016
2016-07-05
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/88527
url https://hdl.handle.net/2117/88527
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institut d'Estadística de Catalunya
publisher.none.fl_str_mv Institut d'Estadística de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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