97
3

Nonparametric Linear Regression for Spatial Data on Graphs with Wavelets

Abstract

Nonparametric regression estimates for dd-dimensional random fields are studied. The data is defined on a not necessarily regular NN-dimensional lattice structure and is strong mixing. We show the consistency and obtain rates of convergence for nonparametric regression estimators which are derived from finite dimensional linear function spaces. As an application, we estimate the regression function with dd-dimensional wavelets which are not necessarily isotropic. We give numerical examples of the estimation procedure where we simulate random fields on planar graphs with the concept of concliques (Kaiser [2012]).

View on arXiv
Comments on this paper