Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces. (English) Zbl 1142.62020
Summary: The author proposes an extension of the 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 the 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.
MSC:
| 62G08 | Nonparametric regression and quantile regression |
| 62J02 | General nonlinear regression |
| 46N30 | Applications of functional analysis in probability theory and statistics |
Keywords:
functional regression model; generalized cross-validation; kernel estimate; representation theoremSoftware:
fda (R)References:
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