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General Framework for Nonlinear Functional Regression with Reproducing Kernel Hilbert Spaces
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.
Cited literature [12 references]
https://hal.inria.fr/inria-00378381
Contributor : Hachem Kadri <>
Submitted on : Friday, April 24, 2009 - 10:48:02 AM
Last modification on : Monday, November 4, 2019 - 12:58:05 PM
Document(s) archivé(s) le : Friday, October 12, 2012 - 5:15:48 PM
Contributor : Hachem Kadri <>
Submitted on : Friday, April 24, 2009 - 10:48:02 AM
Last modification on : Monday, November 4, 2019 - 12:58:05 PM
Document(s) archivé(s) le : Friday, October 12, 2012 - 5:15:48 PM
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RR-6908.pdf
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