Numerical results concerning a sharp adaptive density estimator

We present here a simulation study of the behavior of a particular kernel density estimator. It was previously proven that this nonparametric estimator is sharp in the sense of the minimax adaptive theory, which means that it is equally well performing for very smooth or unsmooth densities. The method selects locally both the bandwidth and the kernel function according to the evaluated smoothness of the underlying density. In this paper we describe the method and apply it successfully to i.i.d. simulated data of different probability densities.