Penalized contrast estimator for adaptive density deconvolution

Abstract

The authors consider the problem of estimating the density $g$ of independent and identically distributed variables $X\_i$, from a sample $Z\_1,...,Z\_n$ where $Z\_i=X\_i+\sigma \epsilon \_i$, $i=1,...,n$, $\epsilon$ is a noise independent of $X$, with $\sigma \epsilon$ having known distribution. They present a model selection procedure allowing to construct an adaptive estimator of $g$ and to find non-asymptotic bounds for its $\mathbb{L}\_2(\mathbb{R})$-risk. The estimator achieves the minimax rate of convergence, in most cases where lowers bounds are available. A simulation study gives an illustration of the good practical performances of the method.