Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces

Heng Lian

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Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces

Heng Lian

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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.

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Heng Lian. 2007-02-05. Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces. https://arxiv.org/abs/math/0702120

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