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Variable selection and estimation in multivariate functional linear regression via the Lasso

hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorRoche, Angelina
dc.date.accessioned2018-09-04T09:24:09Z
dc.date.available2018-09-04T09:24:09Z
dc.date.issued2018
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/17935
dc.language.isoenen
dc.subjectLasso methoden
dc.subject.ddc519en
dc.titleVariable selection and estimation in multivariate functional linear regression via the Lassoen
dc.typeDocument de travail / Working paper
dc.description.abstractenIn more and more applications, a quantity of interest may depend on several covariates, with at least one of them infinite-dimensional (e.g. a curve). To select relevant covariate in this context, we propose an adaptation of the Lasso method. The criterion is based on classical Lasso inference under group sparsity (Yuan and Lin, 2006; Lounici et al., 2011). We give properties of the solution in our infinite-dimensional context. A sparsity-oracle inequality is shown and we propose a coordinate-wise descent algorithm, inspired by the glmnet algorithm (Friedman et al., 2007). A numerical study on simulated and experimental datasets illustrates the behavior of the method.en
dc.identifier.citationpages21en
dc.relation.ispartofseriestitleCahier de recherche CEREMADE, Université Paris-Dauphineen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.identifier.citationdate2018-03
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2018-09-04T09:21:31Z
hal.author.functionaut


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