Robust estimators of accelerated failure time regression with generalized log-gamma errors

Autores
Agostinelli, Claudio; Locatelli, Isabella; Marazzi, Alfio Natale; Yohai, Victor Jaime
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The generalized log-gamma (GLG) model is a very flexible family of distributions to analyze datasets in many different areas of science and technology. Estimators are proposed which are simultaneously highly robust and highly efficient for the parameters of a GLG distribution in the presence of censoring. Estimators with the same properties for accelerated failure time models with censored observations and error distribution belonging to the GLG family are also introduced. It is proven that the proposed estimators are asymptotically fully efficient and the maximum mean square error is examined using Monte Carlo simulations. The simulations confirm that the proposed estimators are highly robust and highly efficient for a finite sample size. Finally, the benefits of the proposed estimators in applications are illustrated with the help of two real datasets.
Fil: Agostinelli, Claudio. University of Trento; Italia
Fil: Locatelli, Isabella. Lausanne University Hospital; Suiza
Fil: Marazzi, Alfio Natale. Lausanne University Hospital; Suiza. Nice Computing SA; Suiza
Fil: Yohai, Victor Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina
Materia
Censored Data
Quantile Distance Estimates
Truncated Maximum Likelihood Estimators
Weighted Likelihood Estimators
Τ 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:11336/66008
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/66008
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Robust estimators of accelerated failure time regression with generalized log-gamma errorsAgostinelli, ClaudioLocatelli, IsabellaMarazzi, Alfio NataleYohai, Victor JaimeCensored DataQuantile Distance EstimatesTruncated Maximum Likelihood EstimatorsWeighted Likelihood EstimatorsΤ Estimatorshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The generalized log-gamma (GLG) model is a very flexible family of distributions to analyze datasets in many different areas of science and technology. Estimators are proposed which are simultaneously highly robust and highly efficient for the parameters of a GLG distribution in the presence of censoring. Estimators with the same properties for accelerated failure time models with censored observations and error distribution belonging to the GLG family are also introduced. It is proven that the proposed estimators are asymptotically fully efficient and the maximum mean square error is examined using Monte Carlo simulations. The simulations confirm that the proposed estimators are highly robust and highly efficient for a finite sample size. Finally, the benefits of the proposed estimators in applications are illustrated with the help of two real datasets.Fil: Agostinelli, Claudio. University of Trento; ItaliaFil: Locatelli, Isabella. Lausanne University Hospital; SuizaFil: Marazzi, Alfio Natale. Lausanne University Hospital; Suiza. Nice Computing SA; SuizaFil: Yohai, Victor Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; ArgentinaElsevier Science2017-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/66008Agostinelli, Claudio; Locatelli, Isabella; Marazzi, Alfio Natale; Yohai, Victor Jaime; Robust estimators of accelerated failure time regression with generalized log-gamma errors; Elsevier Science; Computational Statistics and Data Analysis; 107; 3-2017; 92-1060167-9473CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2016.10.012info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167947316302390info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:53:02Zoai:ri.conicet.gov.ar:11336/66008instacron: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:34982025-09-03 09:53:02.395CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust estimators of accelerated failure time regression with generalized log-gamma errors
title Robust estimators of accelerated failure time regression with generalized log-gamma errors
spellingShingle Robust estimators of accelerated failure time regression with generalized log-gamma errors
Agostinelli, Claudio
Censored Data
Quantile Distance Estimates
Truncated Maximum Likelihood Estimators
Weighted Likelihood Estimators
Τ Estimators
title_short Robust estimators of accelerated failure time regression with generalized log-gamma errors
title_full Robust estimators of accelerated failure time regression with generalized log-gamma errors
title_fullStr Robust estimators of accelerated failure time regression with generalized log-gamma errors
title_full_unstemmed Robust estimators of accelerated failure time regression with generalized log-gamma errors
title_sort Robust estimators of accelerated failure time regression with generalized log-gamma errors
dc.creator.none.fl_str_mv Agostinelli, Claudio
Locatelli, Isabella
Marazzi, Alfio Natale
Yohai, Victor Jaime
author Agostinelli, Claudio
author_facet Agostinelli, Claudio
Locatelli, Isabella
Marazzi, Alfio Natale
Yohai, Victor Jaime
author_role author
author2 Locatelli, Isabella
Marazzi, Alfio Natale
Yohai, Victor Jaime
author2_role author
author
author
dc.subject.none.fl_str_mv Censored Data
Quantile Distance Estimates
Truncated Maximum Likelihood Estimators
Weighted Likelihood Estimators
Τ Estimators
topic Censored Data
Quantile Distance Estimates
Truncated Maximum Likelihood Estimators
Weighted Likelihood Estimators
Τ Estimators
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The generalized log-gamma (GLG) model is a very flexible family of distributions to analyze datasets in many different areas of science and technology. Estimators are proposed which are simultaneously highly robust and highly efficient for the parameters of a GLG distribution in the presence of censoring. Estimators with the same properties for accelerated failure time models with censored observations and error distribution belonging to the GLG family are also introduced. It is proven that the proposed estimators are asymptotically fully efficient and the maximum mean square error is examined using Monte Carlo simulations. The simulations confirm that the proposed estimators are highly robust and highly efficient for a finite sample size. Finally, the benefits of the proposed estimators in applications are illustrated with the help of two real datasets.
Fil: Agostinelli, Claudio. University of Trento; Italia
Fil: Locatelli, Isabella. Lausanne University Hospital; Suiza
Fil: Marazzi, Alfio Natale. Lausanne University Hospital; Suiza. Nice Computing SA; Suiza
Fil: Yohai, Victor Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina
description The generalized log-gamma (GLG) model is a very flexible family of distributions to analyze datasets in many different areas of science and technology. Estimators are proposed which are simultaneously highly robust and highly efficient for the parameters of a GLG distribution in the presence of censoring. Estimators with the same properties for accelerated failure time models with censored observations and error distribution belonging to the GLG family are also introduced. It is proven that the proposed estimators are asymptotically fully efficient and the maximum mean square error is examined using Monte Carlo simulations. The simulations confirm that the proposed estimators are highly robust and highly efficient for a finite sample size. Finally, the benefits of the proposed estimators in applications are illustrated with the help of two real datasets.
publishDate 2017
dc.date.none.fl_str_mv 2017-03
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/11336/66008
Agostinelli, Claudio; Locatelli, Isabella; Marazzi, Alfio Natale; Yohai, Victor Jaime; Robust estimators of accelerated failure time regression with generalized log-gamma errors; Elsevier Science; Computational Statistics and Data Analysis; 107; 3-2017; 92-106
0167-9473
CONICET Digital
CONICET
url http://hdl.handle.net/11336/66008
identifier_str_mv Agostinelli, Claudio; Locatelli, Isabella; Marazzi, Alfio Natale; Yohai, Victor Jaime; Robust estimators of accelerated failure time regression with generalized log-gamma errors; Elsevier Science; Computational Statistics and Data Analysis; 107; 3-2017; 92-106
0167-9473
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2016.10.012
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0167947316302390
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 Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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 13.13397

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