Estimating sufficient reductions of the predictors in abundant high-dimensional regressions

Autores
Cook, R. Dennis; Forzani, Liliana Maria; Rothman, Adam
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various alignments. It is demonstrated that these methods are consistent in a variety of settings, particularly in abundant regressions where most predictors contribute some information on the response, and oracle rates are possible. Simulation results are presented to support the theoretical conclusion.
Fil: Cook, R. Dennis. University of Minnesota; Estados Unidos
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Fil: Rothman, Adam. University of Minnesota; Estados Unidos
Materia
CENTRAL SUBSPACE
ORACLE PROPERTY
PRINCIPAL FITTED COMPONENTS
SPARSITY
SPICE
SUFFICIENT DIMENSION REDUCTION
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:11336/60500
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/60500
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Estimating sufficient reductions of the predictors in abundant high-dimensional regressionsCook, R. DennisForzani, Liliana MariaRothman, AdamCENTRAL SUBSPACEORACLE PROPERTYPRINCIPAL FITTED COMPONENTSSPARSITYSPICESUFFICIENT DIMENSION REDUCTIONhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various alignments. It is demonstrated that these methods are consistent in a variety of settings, particularly in abundant regressions where most predictors contribute some information on the response, and oracle rates are possible. Simulation results are presented to support the theoretical conclusion.Fil: Cook, R. Dennis. University of Minnesota; Estados UnidosFil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; ArgentinaFil: Rothman, Adam. University of Minnesota; Estados UnidosInstitute of Mathematical Statistics2012-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/60500Cook, R. Dennis; Forzani, Liliana Maria; Rothman, Adam; Estimating sufficient reductions of the predictors in abundant high-dimensional regressions; Institute of Mathematical Statistics; Annals Of Statistics, The; 40; 1; 2-2012; 353-3840090-5364CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1214/11-AOS962info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:04:51Zoai:ri.conicet.gov.ar:11336/60500instacron: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:34982025-10-22 11:04:52.105CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
title Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
spellingShingle Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
Cook, R. Dennis
CENTRAL SUBSPACE
ORACLE PROPERTY
PRINCIPAL FITTED COMPONENTS
SPARSITY
SPICE
SUFFICIENT DIMENSION REDUCTION
title_short Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
title_full Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
title_fullStr Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
title_full_unstemmed Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
title_sort Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
dc.creator.none.fl_str_mv Cook, R. Dennis
Forzani, Liliana Maria
Rothman, Adam
author Cook, R. Dennis
author_facet Cook, R. Dennis
Forzani, Liliana Maria
Rothman, Adam
author_role author
author2 Forzani, Liliana Maria
Rothman, Adam
author2_role author
author
dc.subject.none.fl_str_mv CENTRAL SUBSPACE
ORACLE PROPERTY
PRINCIPAL FITTED COMPONENTS
SPARSITY
SPICE
SUFFICIENT DIMENSION REDUCTION
topic CENTRAL SUBSPACE
ORACLE PROPERTY
PRINCIPAL FITTED COMPONENTS
SPARSITY
SPICE
SUFFICIENT DIMENSION REDUCTION
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 study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various alignments. It is demonstrated that these methods are consistent in a variety of settings, particularly in abundant regressions where most predictors contribute some information on the response, and oracle rates are possible. Simulation results are presented to support the theoretical conclusion.
Fil: Cook, R. Dennis. University of Minnesota; Estados Unidos
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Fil: Rothman, Adam. University of Minnesota; Estados Unidos
description We study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various alignments. It is demonstrated that these methods are consistent in a variety of settings, particularly in abundant regressions where most predictors contribute some information on the response, and oracle rates are possible. Simulation results are presented to support the theoretical conclusion.
publishDate 2012
dc.date.none.fl_str_mv 2012-02
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/11336/60500
Cook, R. Dennis; Forzani, Liliana Maria; Rothman, Adam; Estimating sufficient reductions of the predictors in abundant high-dimensional regressions; Institute of Mathematical Statistics; Annals Of Statistics, The; 40; 1; 2-2012; 353-384
0090-5364
CONICET Digital
CONICET
url http://hdl.handle.net/11336/60500
identifier_str_mv Cook, R. Dennis; Forzani, Liliana Maria; Rothman, Adam; Estimating sufficient reductions of the predictors in abundant high-dimensional regressions; Institute of Mathematical Statistics; Annals Of Statistics, The; 40; 1; 2-2012; 353-384
0090-5364
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1214/11-AOS962
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 Institute of Mathematical Statistics
publisher.none.fl_str_mv Institute of Mathematical Statistics
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 12.8982525

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