Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces

Heng Lian

Paper

Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces

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.