Multiple functional regression with both discrete and continuous covariates
Machine Learning
2013-01-16 v1 Machine Learning
Abstract
In this paper we present a nonparametric method for extending functional regression methodology to the situation where more than one functional covariate is used to predict a functional response. Borrowing the idea from Kadri et al. (2010a), the method, which support mixed discrete and continuous explanatory variables, is based on estimating a function-valued function in reproducing kernel Hilbert spaces by virtue of positive operator-valued kernels.
Cite
@article{arxiv.1301.2656,
title = {Multiple functional regression with both discrete and continuous covariates},
author = {Hachem Kadri and Philippe Preux and Emmanuel Duflos and Stéphane Canu},
journal= {arXiv preprint arXiv:1301.2656},
year = {2013}
}