Variable selection and estimation in multivariate functional linear regression via the Lasso
Roche, Angelina (2018), Variable selection and estimation in multivariate functional linear regression via the Lasso. https://basepub.dauphine.fr/handle/123456789/17935
TypeDocument de travail / Working paper
Series titleCahier de recherche CEREMADE, Université Paris-Dauphine
MetadataShow full item record
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)In 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.
Subjects / KeywordsLasso method
Showing items related by title and author.