Adaptive global thresholding on the sphere

This work is concerned with the study of the adaptivity properties of nonparametric regression estimators over the dd-dimensional sphere within the global thresholding framework. The estimators are constructed by means of a form of spherical wavelets, the so-called needlets, which enjoy strong concentration properties in both harmonic and real domains. The author establishes the convergence rates of the LpLp-risks of these estimators, focusing on their minimax properties and proving their optimality over a scale of nonparametric regularity function spaces, namely, the Besov spaces.