Advances on nonparametric regression for functional variables
Statistics Theory
2016-08-16 v1 Statistics Theory
Abstract
We consider the problem of predicting a real random variable from a functional explanatory variable. The problem is attacked by mean of nonparametric kernel approach which has been recently adapted to this functional context. We derive theoretical results by giving a deep asymptotic study of the behaviour of the estimate, including mean squared convergence (with rates and precise evaluation of the constant terms) as well as asymptotic distribution. Practical use of these results are relying on the ability to estimate these constants. Some perspectives in this direction are discussed including the presentation of a functional version of bootstrapping ideas.
Cite
@article{arxiv.math/0603084,
title = {Advances on nonparametric regression for functional variables},
author = {Frédéric Ferraty and André Mas and Philippe Vieu},
journal= {arXiv preprint arXiv:math/0603084},
year = {2016}
}