Nonlinear functional models for functional responses in reproducing kernel hilbert spaces

Abstract

Abstract 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.