Gaussian approximation of nonlinear statistics on the sphere

We show how it is possible to assess the rate of convergence in the Gaussian approximation of triangular arrays of U-statistics, built from wavelets coefficients evaluated on a spherical Poisson field of arbitrary dimension. For this purpose, we exploit the Stein–Malliavin approach introduced in the seminal paper by Peccati, Solé, Taqqu and Utzet (2011); we focus in particular on statistical applications covering evaluation of variance in non-parametric density estimation and Sobolev tests for uniformity.