A method for variable selection in a multivariate functional linear regression model
Statistics Theory
2023-11-03 v1 Statistics Theory
Abstract
We propose a new variable selection procedure for a functional linear model with multiple scalar responses and multiple functional predictors. This method is based on basis expansions of the involved functional predictors and coefficients that lead to a multivariate linear regression model. Then a criterion by means of which the variable selection problem reduces to that of estimating a suitable set is introduced. Estimation of this set is achieved by using appropriate penalizations of estimates of this criterion, so leading to our proposal. A simulation study that permits to investigate the effectiveness of the proposed approach and to compare it with existing methods is given.
Cite
@article{arxiv.2311.00803,
title = {A method for variable selection in a multivariate functional linear regression model},
author = {Alban Mina Mbina and Guy Martial Nkiet},
journal= {arXiv preprint arXiv:2311.00803},
year = {2023}
}