Likelihood-Based Sufficient Dimension Reduction

Autores
Cook, R. Dennis; Forzani, Liliana Maria
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality.
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
Materia
Central Subspace
Directional Regression
Grassmann Manifolds
Sliced Average Variance Estimation
Sliced Inverse Regression
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:11336/84065
Acceder
id CONICETDig_a3a2753f3fd2e800d2bb545d0b3b2301
oai_identifier_str oai:ri.conicet.gov.ar:11336/84065
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Likelihood-Based Sufficient Dimension ReductionCook, R. DennisForzani, Liliana MariaCentral SubspaceDirectional RegressionGrassmann ManifoldsSliced Average Variance EstimationSliced Inverse Regressionhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality.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; ArgentinaAmerican Statistical Association2009-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/84065Cook, R. Dennis; Forzani, Liliana Maria; Likelihood-Based Sufficient Dimension Reduction; American Statistical Association; Journal of The American Statistical Association; 104; 485; 3-2009; 197-2080162-1459CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1198/jasa.2009.0106info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:15:03Zoai:ri.conicet.gov.ar:11336/84065instacron: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:34982025-10-15 15:15:03.766CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Likelihood-Based Sufficient Dimension Reduction
title Likelihood-Based Sufficient Dimension Reduction
spellingShingle Likelihood-Based Sufficient Dimension Reduction
Cook, R. Dennis
Central Subspace
Directional Regression
Grassmann Manifolds
Sliced Average Variance Estimation
Sliced Inverse Regression
title_short Likelihood-Based Sufficient Dimension Reduction
title_full Likelihood-Based Sufficient Dimension Reduction
title_fullStr Likelihood-Based Sufficient Dimension Reduction
title_full_unstemmed Likelihood-Based Sufficient Dimension Reduction
title_sort Likelihood-Based Sufficient Dimension Reduction
dc.creator.none.fl_str_mv Cook, R. Dennis
Forzani, Liliana Maria
author Cook, R. Dennis
author_facet Cook, R. Dennis
Forzani, Liliana Maria
author_role author
author2 Forzani, Liliana Maria
author2_role author
dc.subject.none.fl_str_mv Central Subspace
Directional Regression
Grassmann Manifolds
Sliced Average Variance Estimation
Sliced Inverse Regression
topic Central Subspace
Directional Regression
Grassmann Manifolds
Sliced Average Variance Estimation
Sliced Inverse Regression
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 obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality.
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
description We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality.
publishDate 2009
dc.date.none.fl_str_mv 2009-03
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/11336/84065
Cook, R. Dennis; Forzani, Liliana Maria; Likelihood-Based Sufficient Dimension Reduction; American Statistical Association; Journal of The American Statistical Association; 104; 485; 3-2009; 197-208
0162-1459
CONICET Digital
CONICET
url http://hdl.handle.net/11336/84065
identifier_str_mv Cook, R. Dennis; Forzani, Liliana Maria; Likelihood-Based Sufficient Dimension Reduction; American Statistical Association; Journal of The American Statistical Association; 104; 485; 3-2009; 197-208
0162-1459
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1198/jasa.2009.0106
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 American Statistical Association
publisher.none.fl_str_mv American Statistical Association
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_ 1846083298301837312
score 13.22299

Publicaciones similares