Robust Estimators of the Generalized Log-Gamma Distribution

Autores
Agostinelli, Claudio; Marazzi, Alfio Natale; Yohai, Victor Jaime
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We propose robust estimators of the generalized log-gamma distribution and, more generally, of location-shape-scale families of distributions. A (weighted) Qτ estimator minimizes a τ scale of the differences between empirical and theoretical quantiles. It is n1/2 consistent; unfortunately, it is not asymptotically normal and, therefore, inconvenient for inference. However, it is a convenient starting point for a one-step weighted likelihood estimator, where the weights are based on a disparity measure between the model density and a kernel density estimate. The one-step weighted likelihood estimator is asymptotically normal and fully efficient under the model. It is also highly robust under outlier contamination. Supplementary materials are available online.
Fil: Agostinelli, Claudio. Universita' Ca' Foscari Di Venezia; Italia
Fil: Marazzi, Alfio Natale. Universite de Lausanne; Suiza
Fil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Minimum Quantile Distance Estimators
Τ - Estimators
Weighted Likelihood Estimators
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/30647
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/30647
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Robust Estimators of the Generalized Log-Gamma DistributionAgostinelli, ClaudioMarazzi, Alfio NataleYohai, Victor JaimeMinimum Quantile Distance EstimatorsΤ - EstimatorsWeighted Likelihood Estimatorshttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We propose robust estimators of the generalized log-gamma distribution and, more generally, of location-shape-scale families of distributions. A (weighted) Qτ estimator minimizes a τ scale of the differences between empirical and theoretical quantiles. It is n1/2 consistent; unfortunately, it is not asymptotically normal and, therefore, inconvenient for inference. However, it is a convenient starting point for a one-step weighted likelihood estimator, where the weights are based on a disparity measure between the model density and a kernel density estimate. The one-step weighted likelihood estimator is asymptotically normal and fully efficient under the model. It is also highly robust under outlier contamination. Supplementary materials are available online.Fil: Agostinelli, Claudio. Universita' Ca' Foscari Di Venezia; ItaliaFil: Marazzi, Alfio Natale. Universite de Lausanne; SuizaFil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaTaylor & Francis2013-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/30647Agostinelli, Claudio; Marazzi, Alfio Natale; Yohai, Victor Jaime; Robust Estimators of the Generalized Log-Gamma Distribution; Taylor & Francis; Technometrics; 56; 1; 7-2013; 92-1010040-1706CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1080/00401706.2013.818578info:eu-repo/semantics/altIdentifier/url/http://www.tandfonline.com/doi/abs/10.1080/00401706.2013.818578info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:39:43Zoai:ri.conicet.gov.ar:11336/30647instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-12 09:39:43.902CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust Estimators of the Generalized Log-Gamma Distribution
title Robust Estimators of the Generalized Log-Gamma Distribution
spellingShingle Robust Estimators of the Generalized Log-Gamma Distribution
Agostinelli, Claudio
Minimum Quantile Distance Estimators
Τ - Estimators
Weighted Likelihood Estimators
title_short Robust Estimators of the Generalized Log-Gamma Distribution
title_full Robust Estimators of the Generalized Log-Gamma Distribution
title_fullStr Robust Estimators of the Generalized Log-Gamma Distribution
title_full_unstemmed Robust Estimators of the Generalized Log-Gamma Distribution
title_sort Robust Estimators of the Generalized Log-Gamma Distribution
dc.creator.none.fl_str_mv Agostinelli, Claudio
Marazzi, Alfio Natale
Yohai, Victor Jaime
author Agostinelli, Claudio
author_facet Agostinelli, Claudio
Marazzi, Alfio Natale
Yohai, Victor Jaime
author_role author
author2 Marazzi, Alfio Natale
Yohai, Victor Jaime
author2_role author
author
dc.subject.none.fl_str_mv Minimum Quantile Distance Estimators
Τ - Estimators
Weighted Likelihood Estimators
topic Minimum Quantile Distance Estimators
Τ - Estimators
Weighted Likelihood Estimators
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We propose robust estimators of the generalized log-gamma distribution and, more generally, of location-shape-scale families of distributions. A (weighted) Qτ estimator minimizes a τ scale of the differences between empirical and theoretical quantiles. It is n1/2 consistent; unfortunately, it is not asymptotically normal and, therefore, inconvenient for inference. However, it is a convenient starting point for a one-step weighted likelihood estimator, where the weights are based on a disparity measure between the model density and a kernel density estimate. The one-step weighted likelihood estimator is asymptotically normal and fully efficient under the model. It is also highly robust under outlier contamination. Supplementary materials are available online.
Fil: Agostinelli, Claudio. Universita' Ca' Foscari Di Venezia; Italia
Fil: Marazzi, Alfio Natale. Universite de Lausanne; Suiza
Fil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description We propose robust estimators of the generalized log-gamma distribution and, more generally, of location-shape-scale families of distributions. A (weighted) Qτ estimator minimizes a τ scale of the differences between empirical and theoretical quantiles. It is n1/2 consistent; unfortunately, it is not asymptotically normal and, therefore, inconvenient for inference. However, it is a convenient starting point for a one-step weighted likelihood estimator, where the weights are based on a disparity measure between the model density and a kernel density estimate. The one-step weighted likelihood estimator is asymptotically normal and fully efficient under the model. It is also highly robust under outlier contamination. Supplementary materials are available online.
publishDate 2013
dc.date.none.fl_str_mv 2013-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/30647
Agostinelli, Claudio; Marazzi, Alfio Natale; Yohai, Victor Jaime; Robust Estimators of the Generalized Log-Gamma Distribution; Taylor & Francis; Technometrics; 56; 1; 7-2013; 92-101
0040-1706
CONICET Digital
CONICET
url http://hdl.handle.net/11336/30647
identifier_str_mv Agostinelli, Claudio; Marazzi, Alfio Natale; Yohai, Victor Jaime; Robust Estimators of the Generalized Log-Gamma Distribution; Taylor & Francis; Technometrics; 56; 1; 7-2013; 92-101
0040-1706
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1080/00401706.2013.818578
info:eu-repo/semantics/altIdentifier/url/http://www.tandfonline.com/doi/abs/10.1080/00401706.2013.818578
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.976206

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