Robust Differentiable Functionals for the Additive Hazards Model

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, and th...

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Detalles Bibliográficos
Autores principales: Álvarez, Enrique Ernesto, Ferrario, Julieta
Formato: Articulo
Lenguaje:Inglés
Publicado: 2015
Materias:
Matemática
Robust Estimation
Additive Hazards Model
Survival Analysis
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/81113
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-81113
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Matemática
Robust Estimation
Additive Hazards Model
Survival Analysis
spellingShingle Matemática
Robust Estimation
Additive Hazards Model
Survival Analysis
Álvarez, Enrique Ernesto
Ferrario, Julieta
Robust Differentiable Functionals for the Additive Hazards Model
topic_facet Matemática
Robust Estimation
Additive Hazards Model
Survival Analysis
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, and 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, Álvarez and Ferrarrio (2013) introduced a family of estimators for β which were still highly efficient and asymptotically normal, but they also had bounded influence functions. Those estimators, which are developed using classical Counting Processes methodology, still retain the drawback of having a zero breakdown point.
format Articulo
Articulo
author Álvarez, Enrique Ernesto
Ferrario, Julieta
author_facet Álvarez, Enrique Ernesto
Ferrario, Julieta
author_sort Álvarez, Enrique Ernesto
title Robust Differentiable Functionals for the Additive Hazards Model
title_short Robust Differentiable Functionals for the Additive Hazards Model
title_full Robust Differentiable Functionals for the Additive Hazards Model
title_fullStr Robust Differentiable Functionals for the Additive Hazards Model
title_full_unstemmed Robust Differentiable Functionals for the Additive Hazards Model
title_sort robust differentiable functionals for the additive hazards model
publishDate 2015
url http://sedici.unlp.edu.ar/handle/10915/81113
work_keys_str_mv AT alvarezenriqueernesto robustdifferentiablefunctionalsfortheadditivehazardsmodel
AT ferrariojulieta robustdifferentiablefunctionalsfortheadditivehazardsmodel
bdutipo_str Repositorios
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