  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.
