Resistant estimates for high dimensional and functional data based on random projections

Autores
Fraiman, Jacob Ricardo; Svarc, Marcela
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We herein propose a new robust estimation method based on random projections that is adaptive and automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some restricted contamination models, the procedure is robust and attains full efficiency. We tested the method using both simulated and real data.
Fil: Fraiman, Jacob Ricardo. Universidad de San Andrés; Argentina. Universidad de la República; Uruguay. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Svarc, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina
Materia
Robust estimates
High dimensional data
Trimming procedures
Trimming estimates
Location and scatter estimates
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/22882
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/22882
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Resistant estimates for high dimensional and functional data based on random projectionsFraiman, Jacob RicardoSvarc, MarcelaRobust estimatesHigh dimensional dataTrimming proceduresTrimming estimatesLocation and scatter estimateshttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We herein propose a new robust estimation method based on random projections that is adaptive and automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some restricted contamination models, the procedure is robust and attains full efficiency. We tested the method using both simulated and real data.Fil: Fraiman, Jacob Ricardo. Universidad de San Andrés; Argentina. Universidad de la República; Uruguay. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Svarc, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; ArgentinaElsevier2012-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/22882Fraiman, Jacob Ricardo; Svarc, Marcela; Resistant estimates for high dimensional and functional data based on random projections; Elsevier; Computational Statistics and Data Analysis; 58; 9-2012; 326-3380167-9473CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2012.09.006info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167947312003350info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:00:50Zoai:ri.conicet.gov.ar:11336/22882instacron: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 10:00:50.697CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Resistant estimates for high dimensional and functional data based on random projections
title Resistant estimates for high dimensional and functional data based on random projections
spellingShingle Resistant estimates for high dimensional and functional data based on random projections
Fraiman, Jacob Ricardo
Robust estimates
High dimensional data
Trimming procedures
Trimming estimates
Location and scatter estimates
title_short Resistant estimates for high dimensional and functional data based on random projections
title_full Resistant estimates for high dimensional and functional data based on random projections
title_fullStr Resistant estimates for high dimensional and functional data based on random projections
title_full_unstemmed Resistant estimates for high dimensional and functional data based on random projections
title_sort Resistant estimates for high dimensional and functional data based on random projections
dc.creator.none.fl_str_mv Fraiman, Jacob Ricardo
Svarc, Marcela
author Fraiman, Jacob Ricardo
author_facet Fraiman, Jacob Ricardo
Svarc, Marcela
author_role author
author2 Svarc, Marcela
author2_role author
dc.subject.none.fl_str_mv Robust estimates
High dimensional data
Trimming procedures
Trimming estimates
Location and scatter estimates
topic Robust estimates
High dimensional data
Trimming procedures
Trimming estimates
Location and scatter estimates
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We herein propose a new robust estimation method based on random projections that is adaptive and automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some restricted contamination models, the procedure is robust and attains full efficiency. We tested the method using both simulated and real data.
Fil: Fraiman, Jacob Ricardo. Universidad de San Andrés; Argentina. Universidad de la República; Uruguay. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Svarc, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina
description We herein propose a new robust estimation method based on random projections that is adaptive and automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some restricted contamination models, the procedure is robust and attains full efficiency. We tested the method using both simulated and real data.
publishDate 2012
dc.date.none.fl_str_mv 2012-09
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/11336/22882
Fraiman, Jacob Ricardo; Svarc, Marcela; Resistant estimates for high dimensional and functional data based on random projections; Elsevier; Computational Statistics and Data Analysis; 58; 9-2012; 326-338
0167-9473
CONICET Digital
CONICET
url http://hdl.handle.net/11336/22882
identifier_str_mv Fraiman, Jacob Ricardo; Svarc, Marcela; Resistant estimates for high dimensional and functional data based on random projections; Elsevier; Computational Statistics and Data Analysis; 58; 9-2012; 326-338
0167-9473
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2012.09.006
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167947312003350
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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 13.070432

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