Robust bootstrap: an alternative to bootstrapping robust estimators

Autores
Amado, Conceicao; Bianco, Ana Maria; Boente Boente, Graciela Lina; Pires, Ana M.
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
There is a vast literature on robust estimators, but in some situations it is still not easy to make inferences, such as confidence regions and hypothesis testing. This is mainly due to the following facts. On one hand, in most situations, it is difficult to derive the exact distribution of the estimator. On the other one, even if its asymptotic behaviour is known, in many cases, the convergence to the limiting distribution may be rather slow, so bootstrap methods are preferable since they often give better small sample results. However, resampling methods have several disadvantages including the propagation of anomalous data all along the new samples. In this paper, we discuss the problems arising in the bootstrap when outlying observations are present. We argue that it is preferable to use a robust bootstrap rather than to bootstrap robust estimators and we discuss a robust bootstrap method, the Influence Function Bootstrap denoted IFB. We illustrate the performance of the IFB intervals in the univariate location case and in the logistic regression model. We derive some asymptotic properties of the IFB. Finally, we introduce a generalization of the Influence Function Bootstrap in order to improve the IFB behaviour.
Fil: Amado, Conceicao. Universidade de Lisboa; Portugal
Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santalo". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santalo"; Argentina
Fil: Pires, Ana M.. Universidade de Lisboa; Portugal
Materia
INFLUENCE FUNCTION
RESAMPLING METHODS
ROBUST INFERENCE
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:11336/18748
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Robust bootstrap: an alternative to bootstrapping robust estimatorsAmado, ConceicaoBianco, Ana MariaBoente Boente, Graciela LinaPires, Ana M.INFLUENCE FUNCTIONRESAMPLING METHODSROBUST INFERENCEhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1There is a vast literature on robust estimators, but in some situations it is still not easy to make inferences, such as confidence regions and hypothesis testing. This is mainly due to the following facts. On one hand, in most situations, it is difficult to derive the exact distribution of the estimator. On the other one, even if its asymptotic behaviour is known, in many cases, the convergence to the limiting distribution may be rather slow, so bootstrap methods are preferable since they often give better small sample results. However, resampling methods have several disadvantages including the propagation of anomalous data all along the new samples. In this paper, we discuss the problems arising in the bootstrap when outlying observations are present. We argue that it is preferable to use a robust bootstrap rather than to bootstrap robust estimators and we discuss a robust bootstrap method, the Influence Function Bootstrap denoted IFB. We illustrate the performance of the IFB intervals in the univariate location case and in the logistic regression model. We derive some asymptotic properties of the IFB. Finally, we introduce a generalization of the Influence Function Bootstrap in order to improve the IFB behaviour.Fil: Amado, Conceicao. Universidade de Lisboa; PortugalFil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santalo". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santalo"; ArgentinaFil: Pires, Ana M.. Universidade de Lisboa; PortugalInstituto Nacional de Estatística2014-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/18748Amado, Conceicao; Bianco, Ana Maria; Boente Boente, Graciela Lina; Pires, Ana M.; Robust bootstrap: an alternative to bootstrapping robust estimators; Instituto Nacional de Estatística; Revstat Statistical Journal; 12; 2; 6-2014; 169-1971645-6726CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.ine.pt/revstat/pdf/rs140205.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:52:05Zoai:ri.conicet.gov.ar:11336/18748instacron: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 09:52:06.126CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Robust bootstrap: an alternative to bootstrapping robust estimators
title Robust bootstrap: an alternative to bootstrapping robust estimators
spellingShingle Robust bootstrap: an alternative to bootstrapping robust estimators
Amado, Conceicao
INFLUENCE FUNCTION
RESAMPLING METHODS
ROBUST INFERENCE
title_short Robust bootstrap: an alternative to bootstrapping robust estimators
title_full Robust bootstrap: an alternative to bootstrapping robust estimators
title_fullStr Robust bootstrap: an alternative to bootstrapping robust estimators
title_full_unstemmed Robust bootstrap: an alternative to bootstrapping robust estimators
title_sort Robust bootstrap: an alternative to bootstrapping robust estimators
dc.creator.none.fl_str_mv Amado, Conceicao
Bianco, Ana Maria
Boente Boente, Graciela Lina
Pires, Ana M.
author Amado, Conceicao
author_facet Amado, Conceicao
Bianco, Ana Maria
Boente Boente, Graciela Lina
Pires, Ana M.
author_role author
author2 Bianco, Ana Maria
Boente Boente, Graciela Lina
Pires, Ana M.
author2_role author
author
author
dc.subject.none.fl_str_mv INFLUENCE FUNCTION
RESAMPLING METHODS
ROBUST INFERENCE
topic INFLUENCE FUNCTION
RESAMPLING METHODS
ROBUST INFERENCE
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv There is a vast literature on robust estimators, but in some situations it is still not easy to make inferences, such as confidence regions and hypothesis testing. This is mainly due to the following facts. On one hand, in most situations, it is difficult to derive the exact distribution of the estimator. On the other one, even if its asymptotic behaviour is known, in many cases, the convergence to the limiting distribution may be rather slow, so bootstrap methods are preferable since they often give better small sample results. However, resampling methods have several disadvantages including the propagation of anomalous data all along the new samples. In this paper, we discuss the problems arising in the bootstrap when outlying observations are present. We argue that it is preferable to use a robust bootstrap rather than to bootstrap robust estimators and we discuss a robust bootstrap method, the Influence Function Bootstrap denoted IFB. We illustrate the performance of the IFB intervals in the univariate location case and in the logistic regression model. We derive some asymptotic properties of the IFB. Finally, we introduce a generalization of the Influence Function Bootstrap in order to improve the IFB behaviour.
Fil: Amado, Conceicao. Universidade de Lisboa; Portugal
Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Boente Boente, Graciela Lina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santalo". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santalo"; Argentina
Fil: Pires, Ana M.. Universidade de Lisboa; Portugal
description There is a vast literature on robust estimators, but in some situations it is still not easy to make inferences, such as confidence regions and hypothesis testing. This is mainly due to the following facts. On one hand, in most situations, it is difficult to derive the exact distribution of the estimator. On the other one, even if its asymptotic behaviour is known, in many cases, the convergence to the limiting distribution may be rather slow, so bootstrap methods are preferable since they often give better small sample results. However, resampling methods have several disadvantages including the propagation of anomalous data all along the new samples. In this paper, we discuss the problems arising in the bootstrap when outlying observations are present. We argue that it is preferable to use a robust bootstrap rather than to bootstrap robust estimators and we discuss a robust bootstrap method, the Influence Function Bootstrap denoted IFB. We illustrate the performance of the IFB intervals in the univariate location case and in the logistic regression model. We derive some asymptotic properties of the IFB. Finally, we introduce a generalization of the Influence Function Bootstrap in order to improve the IFB behaviour.
publishDate 2014
dc.date.none.fl_str_mv 2014-06
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/11336/18748
Amado, Conceicao; Bianco, Ana Maria; Boente Boente, Graciela Lina; Pires, Ana M.; Robust bootstrap: an alternative to bootstrapping robust estimators; Instituto Nacional de Estatística; Revstat Statistical Journal; 12; 2; 6-2014; 169-197
1645-6726
CONICET Digital
CONICET
url http://hdl.handle.net/11336/18748
identifier_str_mv Amado, Conceicao; Bianco, Ana Maria; Boente Boente, Graciela Lina; Pires, Ana M.; Robust bootstrap: an alternative to bootstrapping robust estimators; Instituto Nacional de Estatística; Revstat Statistical Journal; 12; 2; 6-2014; 169-197
1645-6726
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.ine.pt/revstat/pdf/rs140205.pdf
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 Instituto Nacional de Estatística
publisher.none.fl_str_mv Instituto Nacional de Estatística
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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