Deconvolution in white noise with a random blurring function

Thomas Willer (1)

1 LPMA - Laboratoire de Probabilités et Modèles Aléatoires

We consider the problem of denoising a function observed after a convolution with a random filter independent of the noise and satisfying some mean smoothness condition depending on an ill posedness coefficient. We establish the minimax rates for the Lp risk over balls of periodic Besov spaces with respect to the level of noise, and we provide an adaptive estimator achieving these rates up to log factors. Simulations were performed to highlight the effects of the ill posedness and of the distribution of the filter on the efficiency of the estimator.

Mots clés: Adaptive estimation; Deconvolution; Inverse problem; Minimax risk; Nonparametric estimation; Wavelet decomposition

Domaines: Statistiques [math.ST]

Thomas Willer. Deconvolution in white noise with a random blurring function. 2005. ⟨hal-00004851⟩