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Adaptive sparse group LASSO in quantile regression

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Adaptive sparse group LASSO in quantile regression

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dc.contributor.author Mendez-Civieta, Alvaro es_ES
dc.contributor.author Aguilera-Morillo, M. Carmen es_ES
dc.contributor.author Lillo, Rosa E. es_ES
dc.date.accessioned 2021-11-05T14:08:36Z
dc.date.available 2021-11-05T14:08:36Z
dc.date.issued 2021-09 es_ES
dc.identifier.issn 1862-5347 es_ES
dc.identifier.uri http://hdl.handle.net/10251/176337
dc.description.abstract [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. es_ES
dc.description.sponsorship 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. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Advances in Data Analysis and Classification es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject High-dimension es_ES
dc.subject Penalization es_ES
dc.subject Regularization es_ES
dc.subject Prediction es_ES
dc.subject Weight calculation es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Adaptive sparse group LASSO in quantile regression es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11634-020-00413-8 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-88708-P/ES/CONTRIBUCIONES METODOLOGICAS Y APLICADAS EN MODELIZACION ESTOCASTICA Y FUNCIONAL DE DATOS ESTADISTICOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC//UNC313-4E-2361/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//ENE2009-12213-C03-03/ES/Mecanismos Fisicos Implicados En El Transporte Y En Las Transiciones De Confinamiento En Plasmas/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC//ENE2009-12213-C03-03/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//ECO2015-66593-P/ES/"BIG DATA" Y DATOS COMPLEJOS EN EMPRESA Y FINANZAS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC//ENE2012-33219/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//UNC313-4E-2361/ES/PLATAFORMA DE CÁLCULO DE ALTAS PRESTACIONES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Agencia Estatal de Investigación//IJCI-2017-34038//Juan de la Cierva - Incorporación/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Mendez-Civieta, A.; Aguilera-Morillo, MC.; Lillo, RE. (2021). Adaptive sparse group LASSO in quantile regression. Advances in Data Analysis and Classification. 15:547-573. https://doi.org/10.1007/s11634-020-00413-8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11634-020-00413-8 es_ES
dc.description.upvformatpinicio 547 es_ES
dc.description.upvformatpfin 573 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 15 es_ES
dc.relation.pasarela S\429254 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía, Industria y Competitividad es_ES
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