LDR: A Package for Likelihood-Based Sufficient Dimension Reduction

Autores
Cook, R. Dennis; Forzani, Liliana Maria; Tomassi, Diego Rodolfo
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We introduce a new MATLAB software package that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation of d by use of likelihood-ratio testing, permutation testing and information criteria. The methods are suitable for preprocessing data for both regression and classification. Implementations of related estimators are also available. Although the software is more oriented to command-line operation, a graphical user interface is also provided for prototype computations.
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: Tomassi, Diego Rodolfo. 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
DIMENSION REDUCTION
INVERSE REGRESSION
PRINCIPAL COMPONENTS
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:11336/67947
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/67947
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling LDR: A Package for Likelihood-Based Sufficient Dimension ReductionCook, R. DennisForzani, Liliana MariaTomassi, Diego RodolfoDIMENSION REDUCTIONINVERSE REGRESSIONPRINCIPAL COMPONENTShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We introduce a new MATLAB software package that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation of d by use of likelihood-ratio testing, permutation testing and information criteria. The methods are suitable for preprocessing data for both regression and classification. Implementations of related estimators are also available. Although the software is more oriented to command-line operation, a graphical user interface is also provided for prototype computations.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: Tomassi, Diego Rodolfo. 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; ArgentinaJournal Statistical Software2011-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/67947Cook, R. Dennis; Forzani, Liliana Maria; Tomassi, Diego Rodolfo; LDR: A Package for Likelihood-Based Sufficient Dimension Reduction; Journal Statistical Software; Journal Of Statistical Software; 39; 3; 3-2011; 1-11548-7660CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.jstatsoft.org/article/view/v039i03info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:29:08Zoai:ri.conicet.gov.ar:11336/67947instacron: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 11:29:09.167CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv LDR: A Package for Likelihood-Based Sufficient Dimension Reduction
title LDR: A Package for Likelihood-Based Sufficient Dimension Reduction
spellingShingle LDR: A Package for Likelihood-Based Sufficient Dimension Reduction
Cook, R. Dennis
DIMENSION REDUCTION
INVERSE REGRESSION
PRINCIPAL COMPONENTS
title_short LDR: A Package for Likelihood-Based Sufficient Dimension Reduction
title_full LDR: A Package for Likelihood-Based Sufficient Dimension Reduction
title_fullStr LDR: A Package for Likelihood-Based Sufficient Dimension Reduction
title_full_unstemmed LDR: A Package for Likelihood-Based Sufficient Dimension Reduction
title_sort LDR: A Package for Likelihood-Based Sufficient Dimension Reduction
dc.creator.none.fl_str_mv Cook, R. Dennis
Forzani, Liliana Maria
Tomassi, Diego Rodolfo
author Cook, R. Dennis
author_facet Cook, R. Dennis
Forzani, Liliana Maria
Tomassi, Diego Rodolfo
author_role author
author2 Forzani, Liliana Maria
Tomassi, Diego Rodolfo
author2_role author
author
dc.subject.none.fl_str_mv DIMENSION REDUCTION
INVERSE REGRESSION
PRINCIPAL COMPONENTS
topic DIMENSION REDUCTION
INVERSE REGRESSION
PRINCIPAL COMPONENTS
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 introduce a new MATLAB software package that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation of d by use of likelihood-ratio testing, permutation testing and information criteria. The methods are suitable for preprocessing data for both regression and classification. Implementations of related estimators are also available. Although the software is more oriented to command-line operation, a graphical user interface is also provided for prototype computations.
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: Tomassi, Diego Rodolfo. 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 introduce a new MATLAB software package that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation of d by use of likelihood-ratio testing, permutation testing and information criteria. The methods are suitable for preprocessing data for both regression and classification. Implementations of related estimators are also available. Although the software is more oriented to command-line operation, a graphical user interface is also provided for prototype computations.
publishDate 2011
dc.date.none.fl_str_mv 2011-03
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/11336/67947
Cook, R. Dennis; Forzani, Liliana Maria; Tomassi, Diego Rodolfo; LDR: A Package for Likelihood-Based Sufficient Dimension Reduction; Journal Statistical Software; Journal Of Statistical Software; 39; 3; 3-2011; 1-1
1548-7660
CONICET Digital
CONICET
url http://hdl.handle.net/11336/67947
identifier_str_mv Cook, R. Dennis; Forzani, Liliana Maria; Tomassi, Diego Rodolfo; LDR: A Package for Likelihood-Based Sufficient Dimension Reduction; Journal Statistical Software; Journal Of Statistical Software; 39; 3; 3-2011; 1-1
1548-7660
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.jstatsoft.org/article/view/v039i03
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 Journal Statistical Software
publisher.none.fl_str_mv Journal Statistical Software
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_ 1846781874879332352
score 13.229304

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