  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.
