Nonparametric regression estimation for random fields in a fixed-design
Statistics Theory
2007-06-13 v1 Probability
Statistics Theory
Abstract
We investigate the nonparametric estimation for regression in a fixed-design setting when the errors are given by a field of dependent random variables. Sufficient conditions for kernel estimators to converge uniformly are obtained. These estimators can attain the optimal rates of uniform convergence and the results apply to a large class of random fields which contains martingale-difference random fields and mixing random fields.
Cite
@article{arxiv.math/0502091,
title = {Nonparametric regression estimation for random fields in a fixed-design},
author = {Mohamed El Machkouri},
journal= {arXiv preprint arXiv:math/0502091},
year = {2007}
}
Comments
Accept\'{e} pour publication dans la revue "Statistical Inference for Stochastic Processes"