Abstract
This presentation aims to introduce an approach for dealing with sparse regression models when functional variables are involved in the statistical sample. The idea is not to restrict to any specific variable selection procedure, but rather to present a two-stage methodology allowing to adapt efficiently any multivariate procedure to the functional framework. These ideas can be applied to any kind of functional regression models, including linear, semi-parametric or non-parametric models.
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Aneiros, G., Vieu, P. (2017). A general sparse modeling approach for regression problems involving functional data. In: Aneiros, G., G. Bongiorno, E., Cao, R., Vieu, P. (eds) Functional Statistics and Related Fields. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-55846-2_5
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