2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/131325In the Gaussian white noise model, we study the estimation of an unknown multidimensional function $f$ in the uniform norm by using kernel methods. The performances of procedures are measured by using the maxiset point of view: we determine the set of functions which are well estimated (at a prescribed rate) by each procedure. So, in this paper, we determine the maxisets associated to kernel estimators and to the Lepski procedure for the rate of convergence of the form $(\log n/n)^{\be/(2\be+d)}$. We characterize the maxisets in terms of Besov and Hölder spaces of regularity $β$.25 pagesStatistics Theory62G07, 62G20text