Asymptotic normality of kernel estimates in a regression model for random fields
Statistics Theory
2009-07-10 v1 Statistics Theory
Abstract
We establish the asymptotic normality of the regression estimator in a fixed-design setting when the errors are given by a field of dependent random variables. The result applies to martingale-difference or strongly mixing random fields. On this basis, a statistical test that can be applied to image analysis is also presented.
Cite
@article{arxiv.0907.1519,
title = {Asymptotic normality of kernel estimates in a regression model for random fields},
author = {Mohamed El Machkouri and Radu Stoica},
journal= {arXiv preprint arXiv:0907.1519},
year = {2009}
}
Comments
20 pages