Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces

The author proposes an extension of reproducing kernel Hilbert space theory which provides a new framework for analyzing functional responses with regression models. The approach only presumes a general nonlinear regression structure, as opposed to existing linear regression models. The author proposes generalized cross-validation for automatic smoothing parameter estimation. He illustrates the use of the new estimator both on real and simulated data.