Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio

Lasso regression methods include a penalty function expressed in terms of a norm defined in the space of model coefficients. The norm plays a key role as regards the way coefficients can become irrelevant in the model. For models with a compositional covariate, the norm should be coherent with the A...

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Autores: Saperas Riera, Jordi, Mateu i Figueras, Glòria, Martín Fernández, Josep Antoni
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/24248
Acceso en línea:http://hdl.handle.net/10256/24248
Access Level:acceso abierto
Palabra clave:Anàlisi de regressió
Regression analysis
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spelling Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratioSaperas Riera, JordiMateu i Figueras, GlòriaMartín Fernández, Josep AntoniAnàlisi de regressióRegression analysisLasso regression methods include a penalty function expressed in terms of a norm defined in the space of model coefficients. The norm plays a key role as regards the way coefficients can become irrelevant in the model. For models with a compositional covariate, the norm should be coherent with the Aitchison geometry. The proposed method is based on a newly-defined compositional norm called L1 pairwise logratio. The novel approach allows one to construct an appropriate basis through a sequential binary partition for discriminating between balances that influence the response variable and those that have no effect. This generalised Lasso regression scheme is illustrated with the analysis of a geochemical data setThis research was supported by the Ministerio de Ciencia e Innovación under the project “CODA-GENERA” (Ref. PID2021-123833OB-I00) and the grant PRE2019-090976; and by the Agència de Gestió d'Ajuts Universitaris i de Recerca of the Generalitat de Catalunya under the project “COSDA” (Ref. 2021SGR01197)Open Access funding provided thanks to the CRUE-CSIC agreement with ElsevierElsevierAgencia Estatal de Investigación2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10256/24248http://hdl.handle.net/10256/24248Journal of Geochemical Exploration, 2023, vol. 255, art. núm. 107327Articles publicats (D-IMAE)Saperas Riera, Jordi Mateu i Figueras, Glòria Martín Fernández, Josep Antoni 2023 Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio Journal of Geochemical Exploration 255 107327reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.gexplo.2023.107327info:eu-repo/semantics/altIdentifier/issn/0375-6742info:eu-repo/semantics/altIdentifier/eissn/1879-1689PID2021-123833OB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-123833OB-I00Reconeixement 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessoai:recercat.cat:10256/242482026-05-29T05:05:01Z
dc.title.none.fl_str_mv Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio
title Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio
spellingShingle Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio
Saperas Riera, Jordi
Anàlisi de regressió
Regression analysis
title_short Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio
title_full Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio
title_fullStr Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio
title_full_unstemmed Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio
title_sort Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio
dc.creator.none.fl_str_mv Saperas Riera, Jordi
Mateu i Figueras, Glòria
Martín Fernández, Josep Antoni
author Saperas Riera, Jordi
author_facet Saperas Riera, Jordi
Mateu i Figueras, Glòria
Martín Fernández, Josep Antoni
author_role author
author2 Mateu i Figueras, Glòria
Martín Fernández, Josep Antoni
author2_role author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación
dc.subject.none.fl_str_mv Anàlisi de regressió
Regression analysis
topic Anàlisi de regressió
Regression analysis
description Lasso regression methods include a penalty function expressed in terms of a norm defined in the space of model coefficients. The norm plays a key role as regards the way coefficients can become irrelevant in the model. For models with a compositional covariate, the norm should be coherent with the Aitchison geometry. The proposed method is based on a newly-defined compositional norm called L1 pairwise logratio. The novel approach allows one to construct an appropriate basis through a sequential binary partition for discriminating between balances that influence the response variable and those that have no effect. This generalised Lasso regression scheme is illustrated with the analysis of a geochemical data set
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/24248
http://hdl.handle.net/10256/24248
url http://hdl.handle.net/10256/24248
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.gexplo.2023.107327
info:eu-repo/semantics/altIdentifier/issn/0375-6742
info:eu-repo/semantics/altIdentifier/eissn/1879-1689
PID2021-123833OB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-123833OB-I00
dc.rights.none.fl_str_mv Reconeixement 4.0 Internacional
http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconeixement 4.0 Internacional
http://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Journal of Geochemical Exploration, 2023, vol. 255, art. núm. 107327
Articles publicats (D-IMAE)
Saperas Riera, Jordi Mateu i Figueras, Glòria Martín Fernández, Josep Antoni 2023 Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio Journal of Geochemical Exploration 255 107327
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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