Nonparametric recursive method for kernel-type function estimators for spatial data

In the present paper we propose recursive general kernel-type estimators for spatial data defined by the stochastic approximation algorithm. We obtain the central limit theorem and strong pointwise convergence rate for the nonparametric recursive general kernel-type estimators under some mild conditions. Finally, we investigate the MISE of the proposed estimators and provide the optimal bandwidth.