
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
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Document de travail / Working paperDate
2018Series title
Cahier de recherche CEREMADE, Université Paris-DauphinePages
21
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Show full item recordAbstract (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 / Keywords
Lasso methodRelated items
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