Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection
- Autores
- Bianco, Ana Maria; Boente Boente, Graciela Lina; Rodrigues, Isabel
- Año de publicación
- 2013
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- When dealing with situations in which the responses are discrete or show some type of asymmetry, the linear model is not appropriate to establish the relation between the responses and the covariates. Generalized linear models serve this purpose, since they allow one to model the mean of the responses through a link function, linearly on the covariates. When atypical observations are present in the sample, robust estimators are useful to provide fair estimations as well as to build outlier detection rules. The focus of this paper is to define robust estimators for the regression parameter when missing data possibly occur in the responses. The estimators introduced turn out to be consistent under mild conditions. In particular, resistant methods for Poisson and Gamma models are given. A simulation study allows one to compare the behaviour of the classical and robust estimators, under different contamination schemes. The robustness of the proposed procedures is studied through the influence function, while asymptotic variances are derived from it. Besides, outlier detection rules are defined using the influence function. The procedure is also illustrated by analysing a real data set.
Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rodrigues, Isabel. Technical University of Lisbon; Portugal - Materia
-
Fisher-Consistency
Generalized Lnear Model
Missing Data
Outliers - 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:11336/15863
Ver los metadatos del registro completo
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Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detectionBianco, Ana MariaBoente Boente, Graciela LinaRodrigues, IsabelFisher-ConsistencyGeneralized Lnear ModelMissing DataOutliershttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1When dealing with situations in which the responses are discrete or show some type of asymmetry, the linear model is not appropriate to establish the relation between the responses and the covariates. Generalized linear models serve this purpose, since they allow one to model the mean of the responses through a link function, linearly on the covariates. When atypical observations are present in the sample, robust estimators are useful to provide fair estimations as well as to build outlier detection rules. The focus of this paper is to define robust estimators for the regression parameter when missing data possibly occur in the responses. The estimators introduced turn out to be consistent under mild conditions. In particular, resistant methods for Poisson and Gamma models are given. A simulation study allows one to compare the behaviour of the classical and robust estimators, under different contamination schemes. The robustness of the proposed procedures is studied through the influence function, while asymptotic variances are derived from it. Besides, outlier detection rules are defined using the influence function. The procedure is also illustrated by analysing a real data set.Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rodrigues, Isabel. Technical University of Lisbon; PortugalElsevier Inc2013-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/15863Bianco, Ana Maria; Boente Boente, Graciela Lina; Rodrigues, Isabel; Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection; Elsevier Inc; Journal Of Multivariate Analysis; 114; 2-2013; 209-2260047-259Xenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmva.2012.08.008info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0047259X12002060info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:28:39Zoai:ri.conicet.gov.ar:11336/15863instacron: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:34982025-09-29 10:28:39.75CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection |
title |
Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection |
spellingShingle |
Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection Bianco, Ana Maria Fisher-Consistency Generalized Lnear Model Missing Data Outliers |
title_short |
Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection |
title_full |
Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection |
title_fullStr |
Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection |
title_full_unstemmed |
Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection |
title_sort |
Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection |
dc.creator.none.fl_str_mv |
Bianco, Ana Maria Boente Boente, Graciela Lina Rodrigues, Isabel |
author |
Bianco, Ana Maria |
author_facet |
Bianco, Ana Maria Boente Boente, Graciela Lina Rodrigues, Isabel |
author_role |
author |
author2 |
Boente Boente, Graciela Lina Rodrigues, Isabel |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Fisher-Consistency Generalized Lnear Model Missing Data Outliers |
topic |
Fisher-Consistency Generalized Lnear Model Missing Data Outliers |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.1 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
When dealing with situations in which the responses are discrete or show some type of asymmetry, the linear model is not appropriate to establish the relation between the responses and the covariates. Generalized linear models serve this purpose, since they allow one to model the mean of the responses through a link function, linearly on the covariates. When atypical observations are present in the sample, robust estimators are useful to provide fair estimations as well as to build outlier detection rules. The focus of this paper is to define robust estimators for the regression parameter when missing data possibly occur in the responses. The estimators introduced turn out to be consistent under mild conditions. In particular, resistant methods for Poisson and Gamma models are given. A simulation study allows one to compare the behaviour of the classical and robust estimators, under different contamination schemes. The robustness of the proposed procedures is studied through the influence function, while asymptotic variances are derived from it. Besides, outlier detection rules are defined using the influence function. The procedure is also illustrated by analysing a real data set. Fil: Bianco, Ana Maria. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Boente Boente, Graciela Lina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Rodrigues, Isabel. Technical University of Lisbon; Portugal |
description |
When dealing with situations in which the responses are discrete or show some type of asymmetry, the linear model is not appropriate to establish the relation between the responses and the covariates. Generalized linear models serve this purpose, since they allow one to model the mean of the responses through a link function, linearly on the covariates. When atypical observations are present in the sample, robust estimators are useful to provide fair estimations as well as to build outlier detection rules. The focus of this paper is to define robust estimators for the regression parameter when missing data possibly occur in the responses. The estimators introduced turn out to be consistent under mild conditions. In particular, resistant methods for Poisson and Gamma models are given. A simulation study allows one to compare the behaviour of the classical and robust estimators, under different contamination schemes. The robustness of the proposed procedures is studied through the influence function, while asymptotic variances are derived from it. Besides, outlier detection rules are defined using the influence function. The procedure is also illustrated by analysing a real data set. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-02 |
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/11336/15863 Bianco, Ana Maria; Boente Boente, Graciela Lina; Rodrigues, Isabel; Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection; Elsevier Inc; Journal Of Multivariate Analysis; 114; 2-2013; 209-226 0047-259X |
url |
http://hdl.handle.net/11336/15863 |
identifier_str_mv |
Bianco, Ana Maria; Boente Boente, Graciela Lina; Rodrigues, Isabel; Resistant estimators in Poisson and Gamma models with missing responses and an application to outlier detection; Elsevier Inc; Journal Of Multivariate Analysis; 114; 2-2013; 209-226 0047-259X |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmva.2012.08.008 info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0047259X12002060 |
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 Inc |
publisher.none.fl_str_mv |
Elsevier Inc |
dc.source.none.fl_str_mv |
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) |
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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1844614290859884544 |
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13.070432 |