High breakdown point robust estimators with missing data

Autores
Statti, María Florencia; Sued, Raquel Mariela; Yohai, Victor Jaime
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper, we propose a new procedure to estimate the distribution of a variable y when there are missing data. To compensate the presence of missing responses, it is assumed that a covariate vector x is observed and that y and x are related by means of a semi-parametric regression model. Observed residuals are combined with predicted values to estimate the missing response distribution. Once the responses distribution is consistently estimated, we can estimate any parameter defined through a continuous functional T using a plug in procedure. We prove that the proposed estimators have high breakdown point.
Fil: Statti, María Florencia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Asymptotic Distribution
Breakdown Point
Location And Dispersion Functionals
Missing at Random
Quantiles
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:11336/59978
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/59978
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling High breakdown point robust estimators with missing dataStatti, María FlorenciaSued, Raquel MarielaYohai, Victor JaimeAsymptotic DistributionBreakdown PointLocation And Dispersion FunctionalsMissing at RandomQuantileshttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In this paper, we propose a new procedure to estimate the distribution of a variable y when there are missing data. To compensate the presence of missing responses, it is assumed that a covariate vector x is observed and that y and x are related by means of a semi-parametric regression model. Observed residuals are combined with predicted values to estimate the missing response distribution. Once the responses distribution is consistently estimated, we can estimate any parameter defined through a continuous functional T using a plug in procedure. We prove that the proposed estimators have high breakdown point.Fil: Statti, María Florencia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaTaylor2018-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/59978Statti, María Florencia; Sued, Raquel Mariela; Yohai, Victor Jaime; High breakdown point robust estimators with missing data; Taylor; Communications In Statistics-theory And Methods; 47; 21; 11-2018; 5145-51620361-09261532-415XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1080/03610926.2017.1388396info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.1080/03610926.2017.1388396info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:47:15Zoai:ri.conicet.gov.ar:11336/59978instacron: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 09:47:15.509CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv High breakdown point robust estimators with missing data
title High breakdown point robust estimators with missing data
spellingShingle High breakdown point robust estimators with missing data
Statti, María Florencia
Asymptotic Distribution
Breakdown Point
Location And Dispersion Functionals
Missing at Random
Quantiles
title_short High breakdown point robust estimators with missing data
title_full High breakdown point robust estimators with missing data
title_fullStr High breakdown point robust estimators with missing data
title_full_unstemmed High breakdown point robust estimators with missing data
title_sort High breakdown point robust estimators with missing data
dc.creator.none.fl_str_mv Statti, María Florencia
Sued, Raquel Mariela
Yohai, Victor Jaime
author Statti, María Florencia
author_facet Statti, María Florencia
Sued, Raquel Mariela
Yohai, Victor Jaime
author_role author
author2 Sued, Raquel Mariela
Yohai, Victor Jaime
author2_role author
author
dc.subject.none.fl_str_mv Asymptotic Distribution
Breakdown Point
Location And Dispersion Functionals
Missing at Random
Quantiles
topic Asymptotic Distribution
Breakdown Point
Location And Dispersion Functionals
Missing at Random
Quantiles
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 paper, we propose a new procedure to estimate the distribution of a variable y when there are missing data. To compensate the presence of missing responses, it is assumed that a covariate vector x is observed and that y and x are related by means of a semi-parametric regression model. Observed residuals are combined with predicted values to estimate the missing response distribution. Once the responses distribution is consistently estimated, we can estimate any parameter defined through a continuous functional T using a plug in procedure. We prove that the proposed estimators have high breakdown point.
Fil: Statti, María Florencia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Yohai, Victor Jaime. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description In this paper, we propose a new procedure to estimate the distribution of a variable y when there are missing data. To compensate the presence of missing responses, it is assumed that a covariate vector x is observed and that y and x are related by means of a semi-parametric regression model. Observed residuals are combined with predicted values to estimate the missing response distribution. Once the responses distribution is consistently estimated, we can estimate any parameter defined through a continuous functional T using a plug in procedure. We prove that the proposed estimators have high breakdown point.
publishDate 2018
dc.date.none.fl_str_mv 2018-11
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/11336/59978
Statti, María Florencia; Sued, Raquel Mariela; Yohai, Victor Jaime; High breakdown point robust estimators with missing data; Taylor; Communications In Statistics-theory And Methods; 47; 21; 11-2018; 5145-5162
0361-0926
1532-415X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/59978
identifier_str_mv Statti, María Florencia; Sued, Raquel Mariela; Yohai, Victor Jaime; High breakdown point robust estimators with missing data; Taylor; Communications In Statistics-theory And Methods; 47; 21; 11-2018; 5145-5162
0361-0926
1532-415X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1080/03610926.2017.1388396
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.1080/03610926.2017.1388396
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 Taylor
publisher.none.fl_str_mv Taylor
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
_version_ 1842268845761363968
score 13.13397

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