Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces

Details Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces

2007-02-05

arxiv.org/abs/math/0702120v2

Canadian Journal of Statistics, 35(4):597-606, 2007

Mathematics

Statistics Theory

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Nonlinear functional models for functional responses in reproducing kernel Hilbert spaces will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-427297