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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/59798
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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/ |
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application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Scientific Research Publishing |
publisher.none.fl_str_mv |
Scientific Research Publishing |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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