Multiple functional regression with both discrete and continuous covariates

Hachem Kadri (1) , Philippe Preux (1, 2) , Emmanuel Duflos (1, 3) , Stéphane Canu (4)

1 SEQUEL - Sequential Learning
2 LIFL - Laboratoire d'Informatique Fondamentale de Lille
3 LAGIS-SI
4 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes

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.

Domaines: Machine Learning [stat.ML]; Apprentissage [cs.LG]

Hachem Kadri, Philippe Preux, Emmanuel Duflos, Stéphane Canu. Multiple functional regression with both discrete and continuous covariates. 2nd International Workshop on Functional and Operatorial Statistics (IWFOS), Jun 2011, Santander, Spain. pp.189-195. ⟨hal-00772425⟩