Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces
Statistics Theory
2008-12-17 v2 Statistics Theory
Abstract
An extension of reproducing kernel Hilbert space (RKHS) theory provides a new framework for modeling functional regression models with functional responses. The approach only presumes a general nonlinear regression structure as opposed to previously studied linear regression models. Generalized cross-validation (GCV) is proposed for automatic smoothing parameter estimation. The new RKHS estimate is applied to both simulated and real data as illustrations.
Cite
@article{arxiv.math/0702120,
title = {Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces},
author = {Heng Lian},
journal= {arXiv preprint arXiv:math/0702120},
year = {2008}
}