Abstract : In this paper, we discuss concepts and methods of nonlinear regression for functional data. The focus is on the case where covariates and responses are functions. We present a general framework for modelling functional regression problem in the Reproducing Kernel Hilbert Space (RKHS). Basics concepts of kernel regression analysis in the real case are extended to the domain of functional data analysis. Our main results show how using Hilbert spaces theory to estimate a regression function from observed functional data. This procedure can be thought of as a generalization of scalar-valued nonlinear regression estimate.
https://hal.inria.fr/inria-00378381
Contributeur : Hachem Kadri
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Soumis le : vendredi 24 avril 2009 - 10:48:02
Dernière modification le : lundi 1 octobre 2018 - 09:58:08
Document(s) archivé(s) le : vendredi 12 octobre 2012 - 17:15:48
