  We study non-parametric regression estimates for random fields. The data
satisfies certain strong mixing conditions and is defined on the regular
$N$-dimensional lattice structure. We show consistency and obtain rates of
convergence. The rates are optimal modulo a logarithmic factor in some cases.
As an application, we estimate the regression function with multidimensional
wavelets which are not necessarily isotropic. We simulate random fields on
planar graphs with the concept of concliques (cf. Kaiser et al. [2012]) in
numerical examples of the estimation procedure.
