Abstract
A new sparse semiparametric functional model is proposed, which tries to incorporate the influence of two functional variables in a scalar response in a flexible way, but involving interpretable parameters. One of the functional variables is included trough a single-index structure and the other one linearly, but trough the high-dimensional vector formed by its discretized observations. For this model, a new algorithm for variable selection in the linear part is proposed. This procedure takes advantage of the functional origin of the scalar covariates with linear effect. Some asymptotic results will ensure the good performance of the method. Finally, Tecator’s data will illustrate the great applicability of the presented methodology: good predictive power together with interpretability of the outputs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Novo, S., Aneiros, G., Vieu, P. (2020). Variable Selection in Semiparametric Bi-functional Models. In: Aneiros, G., Horová, I., Hušková, M., Vieu, P. (eds) Functional and High-Dimensional Statistics and Related Fields. IWFOS 2020. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-47756-1_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-47756-1_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-47755-4
Online ISBN: 978-3-030-47756-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)