Robust Differentiable Functionals in the Additive Hazards Model

Autores
Alvarez, Enrique Ernesto; Ferrario, Julieta
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this article we present a new family of estimators for the regression parameter β in the Additive Hazards Model which represents a gain in robustness not only against outliers but also against unspecific contamination schemes. They are consistent and asymptotically normal and furthermore, they have a nonzero breakdown point. In Survival Analysis the Additive Hazards Model proposes a hazard function of the form λ(t) = λ0(t) + β ′ z, where λ0(t) is a common nonparametric baseline hazard function and z is a vector of independent variables. For this model, the seminal work of Lin and Ying (1994) develops an estimator for the regression parameter β which is asymptotically normal and highly efficient. However, a potential drawback of that classical estimator is that it is very sensitive to outliers. In an attempt to gain robustness, Alvarez and Ferrarrio (2013) introduced a family of estimat ´ ors for β which are still highly efficient and asymptotically normal, but they also have bounded influence functions. Those estimators, which were developed using classical Counting Processes methodology, still retain the drawback of having a zero breakdown point. In this article we present a new family of estimators for the regression parameter β in the Additive Hazards Model which represents a gain in robustness not only against outliers but also against unspecific contamination schemes. They are consistent and asymptotically normal and furthermore, they have a nonzero breakdown point.
Fil: Alvarez, Enrique Ernesto. Universidad Nacional de La Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ferrario, Julieta. Universidad Nacional de La Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
ANÁLISIS DE SUPERVIVENCIA
ESTADÍSTICA ROBUSTA
INFERENCIA ESTADÍSTICA
MODELOS DE HAZARDS ADITIVOS
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:11336/59798

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spelling Robust Differentiable Functionals in the Additive Hazards ModelAlvarez, Enrique ErnestoFerrario, JulietaANÁLISIS DE SUPERVIVENCIAESTADÍSTICA ROBUSTAINFERENCIA ESTADÍSTICAMODELOS DE HAZARDS ADITIVOShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In this article we present a new family of estimators for the regression parameter β in the Additive Hazards Model which represents a gain in robustness not only against outliers but also against unspecific contamination schemes. They are consistent and asymptotically normal and furthermore, they have a nonzero breakdown point. In Survival Analysis the Additive Hazards Model proposes a hazard function of the form λ(t) = λ0(t) + β ′ z, where λ0(t) is a common nonparametric baseline hazard function and z is a vector of independent variables. For this model, the seminal work of Lin and Ying (1994) develops an estimator for the regression parameter β which is asymptotically normal and highly efficient. However, a potential drawback of that classical estimator is that it is very sensitive to outliers. In an attempt to gain robustness, Alvarez and Ferrarrio (2013) introduced a family of estimat ´ ors for β which are still highly efficient and asymptotically normal, but they also have bounded influence functions. Those estimators, which were developed using classical Counting Processes methodology, still retain the drawback of having a zero breakdown point. In this article we present a new family of estimators for the regression parameter β in the Additive Hazards Model which represents a gain in robustness not only against outliers but also against unspecific contamination schemes. They are consistent and asymptotically normal and furthermore, they have a nonzero breakdown point.Fil: Alvarez, Enrique Ernesto. Universidad Nacional de La Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ferrario, Julieta. Universidad Nacional de La Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaScientific Research Publishing2015-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/59798Alvarez, Enrique Ernesto; Ferrario, Julieta; Robust Differentiable Functionals in the Additive Hazards Model; Scientific Research Publishing; Open Journal of Statistics; 5; 6; 10-2015; 1-13; 608412161-718XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.4236/ojs.2015.56064info:eu-repo/semantics/altIdentifier/url/http://file.scirp.org/Html/15-1240566_60841.htminfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:50:41Zoai:ri.conicet.gov.ar:11336/59798instacron: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 09:50:41.435CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust Differentiable Functionals in the Additive Hazards Model
title Robust Differentiable Functionals in the Additive Hazards Model
spellingShingle Robust Differentiable Functionals in the Additive Hazards Model
Alvarez, Enrique Ernesto
ANÁLISIS DE SUPERVIVENCIA
ESTADÍSTICA ROBUSTA
INFERENCIA ESTADÍSTICA
MODELOS DE HAZARDS ADITIVOS
title_short Robust Differentiable Functionals in the Additive Hazards Model
title_full Robust Differentiable Functionals in the Additive Hazards Model
title_fullStr Robust Differentiable Functionals in the Additive Hazards Model
title_full_unstemmed Robust Differentiable Functionals in the Additive Hazards Model
title_sort Robust Differentiable Functionals in the Additive Hazards Model
dc.creator.none.fl_str_mv Alvarez, Enrique Ernesto
Ferrario, Julieta
author Alvarez, Enrique Ernesto
author_facet Alvarez, Enrique Ernesto
Ferrario, Julieta
author_role author
author2 Ferrario, Julieta
author2_role author
dc.subject.none.fl_str_mv ANÁLISIS DE SUPERVIVENCIA
ESTADÍSTICA ROBUSTA
INFERENCIA ESTADÍSTICA
MODELOS DE HAZARDS ADITIVOS
topic ANÁLISIS DE SUPERVIVENCIA
ESTADÍSTICA ROBUSTA
INFERENCIA ESTADÍSTICA
MODELOS DE HAZARDS ADITIVOS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this article we present a new family of estimators for the regression parameter β in the Additive Hazards Model which represents a gain in robustness not only against outliers but also against unspecific contamination schemes. They are consistent and asymptotically normal and furthermore, they have a nonzero breakdown point. In Survival Analysis the Additive Hazards Model proposes a hazard function of the form λ(t) = λ0(t) + β ′ z, where λ0(t) is a common nonparametric baseline hazard function and z is a vector of independent variables. For this model, the seminal work of Lin and Ying (1994) develops an estimator for the regression parameter β which is asymptotically normal and highly efficient. However, a potential drawback of that classical estimator is that it is very sensitive to outliers. In an attempt to gain robustness, Alvarez and Ferrarrio (2013) introduced a family of estimat ´ ors for β which are still highly efficient and asymptotically normal, but they also have bounded influence functions. Those estimators, which were developed using classical Counting Processes methodology, still retain the drawback of having a zero breakdown point. In this article we present a new family of estimators for the regression parameter β in the Additive Hazards Model which represents a gain in robustness not only against outliers but also against unspecific contamination schemes. They are consistent and asymptotically normal and furthermore, they have a nonzero breakdown point.
Fil: Alvarez, Enrique Ernesto. Universidad Nacional de La Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ferrario, Julieta. Universidad Nacional de La Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description In this article we present a new family of estimators for the regression parameter β in the Additive Hazards Model which represents a gain in robustness not only against outliers but also against unspecific contamination schemes. They are consistent and asymptotically normal and furthermore, they have a nonzero breakdown point. In Survival Analysis the Additive Hazards Model proposes a hazard function of the form λ(t) = λ0(t) + β ′ z, where λ0(t) is a common nonparametric baseline hazard function and z is a vector of independent variables. For this model, the seminal work of Lin and Ying (1994) develops an estimator for the regression parameter β which is asymptotically normal and highly efficient. However, a potential drawback of that classical estimator is that it is very sensitive to outliers. In an attempt to gain robustness, Alvarez and Ferrarrio (2013) introduced a family of estimat ´ ors for β which are still highly efficient and asymptotically normal, but they also have bounded influence functions. Those estimators, which were developed using classical Counting Processes methodology, still retain the drawback of having a zero breakdown point. In this article we present a new family of estimators for the regression parameter β in the Additive Hazards Model which represents a gain in robustness not only against outliers but also against unspecific contamination schemes. They are consistent and asymptotically normal and furthermore, they have a nonzero breakdown point.
publishDate 2015
dc.date.none.fl_str_mv 2015-10
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/11336/59798
Alvarez, Enrique Ernesto; Ferrario, Julieta; Robust Differentiable Functionals in the Additive Hazards Model; Scientific Research Publishing; Open Journal of Statistics; 5; 6; 10-2015; 1-13; 60841
2161-718X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/59798
identifier_str_mv Alvarez, Enrique Ernesto; Ferrario, Julieta; Robust Differentiable Functionals in the Additive Hazards Model; Scientific Research Publishing; Open Journal of Statistics; 5; 6; 10-2015; 1-13; 60841
2161-718X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.4236/ojs.2015.56064
info:eu-repo/semantics/altIdentifier/url/http://file.scirp.org/Html/15-1240566_60841.htm
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 Scientific Research Publishing
publisher.none.fl_str_mv Scientific Research Publishing
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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