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Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models

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Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models

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dc.contributor.author Laria, Juan C. es_ES
dc.contributor.author Aguilera-Morillo, M. Carmen es_ES
dc.contributor.author Lillo, Rosa E. es_ES
dc.date.accessioned 2023-10-06T18:00:58Z
dc.date.available 2023-10-06T18:00:58Z
dc.date.issued 2022-02 es_ES
dc.identifier.issn 0932-5026 es_ES
dc.identifier.uri http://hdl.handle.net/10251/197838
dc.description.abstract [EN] This paper introduces the Group Linear Algorithm with Sparse Principal decomposition, an algorithm for supervised variable selection and clustering. Our approach extends the Sparse Group Lasso regularization to calculate clusters as part of the model fit. Therefore, unlike Sparse Group Lasso, our idea does not require prior specification of clusters between variables. To determine the clusters, we solve a particular case of sparse Singular Value Decomposition, with a regularization term that follows naturally from the Group Lasso penalty. Moreover, this paper proposes a unified implementation to deal with, but not limited to, linear regression, logistic regression, and proportional hazards models with right-censoring. Our methodology is evaluated using both biological and simulated data, and details of the implementation in R and hyperparameter search are discussed. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Statistical Papers es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Regression es_ES
dc.subject Classification es_ES
dc.subject Feature clustering es_ES
dc.subject Statistical computing es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00362-022-01313-z es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104901RB-I00/ES/NUEVAS ESTRATEGIAS EN REGRESION PENALIZADA CON APLICACIONES EN SALUD, DEMOGRAFIA Y ECONOMIA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.description.bibliographicCitation Laria, JC.; Aguilera-Morillo, MC.; Lillo, RE. (2022). Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models. Statistical Papers. 64(1):227-253. https://doi.org/10.1007/s00362-022-01313-z es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s00362-022-01313-z es_ES
dc.description.upvformatpinicio 227 es_ES
dc.description.upvformatpfin 253 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 64 es_ES
dc.description.issue 1 es_ES
dc.relation.pasarela S\476636 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
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