Nonparametric density estimation for spatial data with wavelets

Abstract

Nonparametric density estimators are studied for d-dimensional, strongly spatial mixing data which are defined on a general N-dimensional lattice structure. We consider linear and nonlinear hard thresholded wavelet estimators derived from a d-dimensional multi-resolution analysis. We give sufficient criteria for the consistency of these estimators and derive rates of convergence in Lp′ for p′∈[1,∞). For this reason, we study density functions which are elements of a d-dimensional Besov space Bp,qs(Rd). We also verify the analytic correctness of our results in numerical simulations.