Sufficient reductions in regression with mixed predictors

Autores
Bura, Efstathia; Forzani, Liliana Maria; García Arancibia, Rodrigo; Llop Orzan, Pamela Nerina; Tomassi, Diego Rodolfo
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Most data sets comprise of measurements on continuous and categorical variables. Yet,modeling high-dimensional mixed predictors has received limited attention in the regressionand classication statistical literature. We study the general regression problem of inferringon a variable of interest based on high dimensional mixed continuous and binary predictors.The aim is to nd a lower dimensional function of the mixed predictor vector that containsall the modeling information in the mixed predictors for the response, which can be eithercontinuous or categorical. The approach we propose identies sucient reductions byreversing the regression and modeling the mixed predictors conditional on the response.We derive the maximum likelihood estimator of the sucient reductions, asymptotic testsfor dimension, and a regularized estimator, which simultaneously achieves variable (feature)selection and dimension reduction (feature extraction). We study the performance of theproposed method and compare it with other approaches through simulations and real dataexamples.
Fil: Bura, Efstathia. Technische Universitat Wien; Austria
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina
Fil: García Arancibia, Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ciencias Económicas. Instituto de Economía Aplicada Litoral; Argentina
Fil: Llop Orzan, Pamela Nerina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina
Fil: Tomassi, Diego Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina
Materia
HIGH DIMENSIONAL
MULTIVARIATE BERNOULLI
REGULARIZATION
FEATURE SELECTION
FEATURE EXTRACTION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/188087
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Sufficient reductions in regression with mixed predictorsBura, EfstathiaForzani, Liliana MariaGarcía Arancibia, RodrigoLlop Orzan, Pamela NerinaTomassi, Diego RodolfoHIGH DIMENSIONALMULTIVARIATE BERNOULLIREGULARIZATIONFEATURE SELECTIONFEATURE EXTRACTIONhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Most data sets comprise of measurements on continuous and categorical variables. Yet,modeling high-dimensional mixed predictors has received limited attention in the regressionand classication statistical literature. We study the general regression problem of inferringon a variable of interest based on high dimensional mixed continuous and binary predictors.The aim is to nd a lower dimensional function of the mixed predictor vector that containsall the modeling information in the mixed predictors for the response, which can be eithercontinuous or categorical. The approach we propose identies sucient reductions byreversing the regression and modeling the mixed predictors conditional on the response.We derive the maximum likelihood estimator of the sucient reductions, asymptotic testsfor dimension, and a regularized estimator, which simultaneously achieves variable (feature)selection and dimension reduction (feature extraction). We study the performance of theproposed method and compare it with other approaches through simulations and real dataexamples.Fil: Bura, Efstathia. Technische Universitat Wien; AustriaFil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; ArgentinaFil: García Arancibia, Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ciencias Económicas. Instituto de Economía Aplicada Litoral; ArgentinaFil: Llop Orzan, Pamela Nerina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; ArgentinaFil: Tomassi, Diego Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; ArgentinaMicrotome2022-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/188087Bura, Efstathia; Forzani, Liliana Maria; García Arancibia, Rodrigo; Llop Orzan, Pamela Nerina; Tomassi, Diego Rodolfo; Sufficient reductions in regression with mixed predictors; Microtome; Journal of Machine Learning Research; 23; 102; 2-2022; 1-461532-4435CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://jmlr.org/papers/v23/21-0175.htmlinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:53:14Zoai:ri.conicet.gov.ar:11336/188087instacron: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 09:53:14.493CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Sufficient reductions in regression with mixed predictors
title Sufficient reductions in regression with mixed predictors
spellingShingle Sufficient reductions in regression with mixed predictors
Bura, Efstathia
HIGH DIMENSIONAL
MULTIVARIATE BERNOULLI
REGULARIZATION
FEATURE SELECTION
FEATURE EXTRACTION
title_short Sufficient reductions in regression with mixed predictors
title_full Sufficient reductions in regression with mixed predictors
title_fullStr Sufficient reductions in regression with mixed predictors
title_full_unstemmed Sufficient reductions in regression with mixed predictors
title_sort Sufficient reductions in regression with mixed predictors
dc.creator.none.fl_str_mv Bura, Efstathia
Forzani, Liliana Maria
García Arancibia, Rodrigo
Llop Orzan, Pamela Nerina
Tomassi, Diego Rodolfo
author Bura, Efstathia
author_facet Bura, Efstathia
Forzani, Liliana Maria
García Arancibia, Rodrigo
Llop Orzan, Pamela Nerina
Tomassi, Diego Rodolfo
author_role author
author2 Forzani, Liliana Maria
García Arancibia, Rodrigo
Llop Orzan, Pamela Nerina
Tomassi, Diego Rodolfo
author2_role author
author
author
author
dc.subject.none.fl_str_mv HIGH DIMENSIONAL
MULTIVARIATE BERNOULLI
REGULARIZATION
FEATURE SELECTION
FEATURE EXTRACTION
topic HIGH DIMENSIONAL
MULTIVARIATE BERNOULLI
REGULARIZATION
FEATURE SELECTION
FEATURE EXTRACTION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Most data sets comprise of measurements on continuous and categorical variables. Yet,modeling high-dimensional mixed predictors has received limited attention in the regressionand classication statistical literature. We study the general regression problem of inferringon a variable of interest based on high dimensional mixed continuous and binary predictors.The aim is to nd a lower dimensional function of the mixed predictor vector that containsall the modeling information in the mixed predictors for the response, which can be eithercontinuous or categorical. The approach we propose identies sucient reductions byreversing the regression and modeling the mixed predictors conditional on the response.We derive the maximum likelihood estimator of the sucient reductions, asymptotic testsfor dimension, and a regularized estimator, which simultaneously achieves variable (feature)selection and dimension reduction (feature extraction). We study the performance of theproposed method and compare it with other approaches through simulations and real dataexamples.
Fil: Bura, Efstathia. Technische Universitat Wien; Austria
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina
Fil: García Arancibia, Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ciencias Económicas. Instituto de Economía Aplicada Litoral; Argentina
Fil: Llop Orzan, Pamela Nerina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina
Fil: Tomassi, Diego Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina
description Most data sets comprise of measurements on continuous and categorical variables. Yet,modeling high-dimensional mixed predictors has received limited attention in the regressionand classication statistical literature. We study the general regression problem of inferringon a variable of interest based on high dimensional mixed continuous and binary predictors.The aim is to nd a lower dimensional function of the mixed predictor vector that containsall the modeling information in the mixed predictors for the response, which can be eithercontinuous or categorical. The approach we propose identies sucient reductions byreversing the regression and modeling the mixed predictors conditional on the response.We derive the maximum likelihood estimator of the sucient reductions, asymptotic testsfor dimension, and a regularized estimator, which simultaneously achieves variable (feature)selection and dimension reduction (feature extraction). We study the performance of theproposed method and compare it with other approaches through simulations and real dataexamples.
publishDate 2022
dc.date.none.fl_str_mv 2022-02
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/11336/188087
Bura, Efstathia; Forzani, Liliana Maria; García Arancibia, Rodrigo; Llop Orzan, Pamela Nerina; Tomassi, Diego Rodolfo; Sufficient reductions in regression with mixed predictors; Microtome; Journal of Machine Learning Research; 23; 102; 2-2022; 1-46
1532-4435
CONICET Digital
CONICET
url http://hdl.handle.net/11336/188087
identifier_str_mv Bura, Efstathia; Forzani, Liliana Maria; García Arancibia, Rodrigo; Llop Orzan, Pamela Nerina; Tomassi, Diego Rodolfo; Sufficient reductions in regression with mixed predictors; Microtome; Journal of Machine Learning Research; 23; 102; 2-2022; 1-46
1532-4435
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://jmlr.org/papers/v23/21-0175.html
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Microtome
publisher.none.fl_str_mv Microtome
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_ 1844613628159852544
score 13.070432

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