  We construct nonparametric confidence sets for regression functions using
wavelets that are uniform over Besov balls. We consider both thresholding and
modulation estimators for the wavelet coefficients. The confidence set is
obtained by showing that a pivot process, constructed from the loss function,
converges uniformly to a mean zero Gaussian process. Inverting this pivot
yields a confidence set for the wavelet coefficients, and from this we obtain
confidence sets on functionals of the regression curve.
