2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/130694We 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.Accepté pour publication dans la revue "Statistical Inference for Stochastic Processes"Statistics TheoryProbability60G60; 62G08text