Adaptive sparse group LASSO in quantile regression

[EN] This paper studies the introduction of sparse group LASSO (SGL) to the quantile regression framework. Additionally, a more flexible version, an adaptive SGL is proposed based on the adaptive idea, this is, the usage of adaptive weights in the penalization. Adaptive estimators are usually focuse...

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Detalles Bibliográficos
Autores: Mendez-Civieta, Alvaro, Lillo, Rosa E., Aguilera-Morillo, M. Carmen|||0000-0003-1027-9773
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/176337
Acceso en línea:https://riunet.upv.es/handle/10251/176337
Access Level:acceso abierto
Palabra clave:High-dimension
Penalization
Regularization
Prediction
Weight calculation
ESTADISTICA E INVESTIGACION OPERATIVA
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spelling Adaptive sparse group LASSO in quantile regressionMendez-Civieta, AlvaroLillo, Rosa E.Aguilera-Morillo, M. Carmen|||0000-0003-1027-9773High-dimensionPenalizationRegularizationPredictionWeight calculationESTADISTICA E INVESTIGACION OPERATIVA[EN] This paper studies the introduction of sparse group LASSO (SGL) to the quantile regression framework. Additionally, a more flexible version, an adaptive SGL is proposed based on the adaptive idea, this is, the usage of adaptive weights in the penalization. Adaptive estimators are usually focused on the study of the oracle property under asymptotic and double asymptotic frameworks. A key step on the demonstration of this property is to consider adaptive weights based on a initial root n-consistent estimator. In practice this implies the usage of a non penalized estimator that limits the adaptive solutions to low dimensional scenarios. In this work, several solutions, based on dimension reduction techniques PCA and PLS, are studied for the calculation of these weights in high dimensional frameworks. The benefits of this proposal are studied both in synthetic and real datasets.We appreciate the work of the referees that has contributed to substantially improve the scientific contributions of this work. In this research we have made use of Uranus, a supercomputer cluster located at University Carlos III of Madrid and funded jointly by EU-FEDER funds and by the Spanish Government via the National Projects No. UNC313-4E-2361, No. ENE2009-12213- C03-03, No. ENE2012-33219 and No. ENE2015-68265-P. This research was partially supported by research grants and Project ECO2015-66593-P from Ministerio de Economia, Industria y Competitividad, Project MTM2017-88708-P from Ministerio de Economia y Competitividad, FEDER funds and Project IJCI-2017-34038 from Agencia Estatal de Investigacion, Ministerio de Ciencia, Innovacion y Universidades.Springer-VerlagDepartamento de Estadística e Investigación Operativa Aplicadas y CalidadEscuela Técnica Superior de Ingeniería IndustrialGrupo de Ingeniería Estadística Multivariante GIEMEuropean CommissionAgencia Estatal de InvestigaciónEuropean Regional Development FundMinisterio de Economía, Industria y CompetitividadRepositorio Institucional de la Universitat Politècnica de València Riunet20212021-09-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/176337reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 MTM2017-88708-P CONTRIBUCIONES METODOLOGICAS Y APLICADAS EN MODELIZACION ESTOCASTICA Y FUNCIONAL DE DATOS ESTADISTICOSMinisterio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 ENE2009-12213-C03-03 Mecanismos Fisicos Implicados En El Transporte Y En Las Transiciones De Confinamiento En PlasmasMinisterio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 ECO2015-66593-P "BIG DATA" Y DATOS COMPLEJOS EN EMPRESA Y FINANZASAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 ENE2012-33219Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 UNC313-4E-2361 PLATAFORMA DE CÁLCULO DE ALTAS PRESTACIONESMinisterio de Ciencia, Innovación y Universidades https://doi.org/10.13039/100014440 Plan Estatal de Investigación Científica, Técnica y de Innovación 2017-2020 IJCI-2017-34038open accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1763372026-06-13T07:49:27Z
dc.title.none.fl_str_mv Adaptive sparse group LASSO in quantile regression
title Adaptive sparse group LASSO in quantile regression
spellingShingle Adaptive sparse group LASSO in quantile regression
Mendez-Civieta, Alvaro
High-dimension
Penalization
Regularization
Prediction
Weight calculation
ESTADISTICA E INVESTIGACION OPERATIVA
title_short Adaptive sparse group LASSO in quantile regression
title_full Adaptive sparse group LASSO in quantile regression
title_fullStr Adaptive sparse group LASSO in quantile regression
title_full_unstemmed Adaptive sparse group LASSO in quantile regression
title_sort Adaptive sparse group LASSO in quantile regression
dc.creator.none.fl_str_mv Mendez-Civieta, Alvaro
Lillo, Rosa E.
Aguilera-Morillo, M. Carmen|||0000-0003-1027-9773
author Mendez-Civieta, Alvaro
author_facet Mendez-Civieta, Alvaro
Lillo, Rosa E.
Aguilera-Morillo, M. Carmen|||0000-0003-1027-9773
author_role author
author2 Lillo, Rosa E.
Aguilera-Morillo, M. Carmen|||0000-0003-1027-9773
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Estadística e Investigación Operativa Aplicadas y Calidad
Escuela Técnica Superior de Ingeniería Industrial
Grupo de Ingeniería Estadística Multivariante GIEM
European Commission
Agencia Estatal de Investigación
European Regional Development Fund
Ministerio de Economía, Industria y Competitividad
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv High-dimension
Penalization
Regularization
Prediction
Weight calculation
ESTADISTICA E INVESTIGACION OPERATIVA
topic High-dimension
Penalization
Regularization
Prediction
Weight calculation
ESTADISTICA E INVESTIGACION OPERATIVA
description [EN] This paper studies the introduction of sparse group LASSO (SGL) to the quantile regression framework. Additionally, a more flexible version, an adaptive SGL is proposed based on the adaptive idea, this is, the usage of adaptive weights in the penalization. Adaptive estimators are usually focused on the study of the oracle property under asymptotic and double asymptotic frameworks. A key step on the demonstration of this property is to consider adaptive weights based on a initial root n-consistent estimator. In practice this implies the usage of a non penalized estimator that limits the adaptive solutions to low dimensional scenarios. In this work, several solutions, based on dimension reduction techniques PCA and PLS, are studied for the calculation of these weights in high dimensional frameworks. The benefits of this proposal are studied both in synthetic and real datasets.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-09-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/176337
url https://riunet.upv.es/handle/10251/176337
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 MTM2017-88708-P CONTRIBUCIONES METODOLOGICAS Y APLICADAS EN MODELIZACION ESTOCASTICA Y FUNCIONAL DE DATOS ESTADISTICOS
Ministerio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 ENE2009-12213-C03-03 Mecanismos Fisicos Implicados En El Transporte Y En Las Transiciones De Confinamiento En Plasmas
Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 ECO2015-66593-P "BIG DATA" Y DATOS COMPLEJOS EN EMPRESA Y FINANZAS
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 ENE2012-33219
Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 UNC313-4E-2361 PLATAFORMA DE CÁLCULO DE ALTAS PRESTACIONES
Ministerio de Ciencia, Innovación y Universidades https://doi.org/10.13039/100014440 Plan Estatal de Investigación Científica, Técnica y de Innovación 2017-2020 IJCI-2017-34038
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer-Verlag
publisher.none.fl_str_mv Springer-Verlag
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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