Convergence in probability of the Mallows and GCV wavelet and Fourier regularization methods

Abstract

Wavelet and Fourier regularization methods are effective for the nonparametric regression problem. We prove that the loss function evaluated for the regularization parameter chosen through GCV or Mallows criteria is asymptotically equivalent in probability to its minimum over the regularization parameter.