Workshop FDA: Registration
View Submission - CRONOSFDA2018
A0184
Title: Variable selection in functional additive regression models Authors:  Manuel Febrero-Bande - University of Santiago de Compostela (Spain) [presenting]
Abstract: The problem of variable selection is considered in the case of functional variables that may be mixed with other type of variables (scalar, multivariate, directional, etc.). Our proposal begins with a simple null model and sequentially selects a new variable to be incorporated into the model based on the use of distance correlation. For the sake of simplicity, only additive models are considered. However, the proposed algorithm may assess the type of contribution (linear, non linear, ...) of each variable. The algorithm has shown quite promising results when applied to simulations and real data sets.