  We consider the problem of estimating the density $g$ of identically
distributed variables $X\_i$, from a sample $Z\_1, ..., Z\_n$ where
$Z\_i=X\_i+\sigma\epsilon\_i$, $i=1, ..., n$ and $\sigma \epsilon\_i$ is a
noise independent of $X\_i$ with known density $
\sigma^{-1}f\_\epsilon(./\sigma)$. We generalize adaptive estimators,
constructed by a model selection procedure, described in Comte et al. (2005).
We study numerically their properties in various contexts and we test their
robustness. Comparisons are made with respect to deconvolution kernel
estimators, misspecification of errors, dependency,... It appears that our
estimation algorithm, based on a fast procedure, performs very well in all
contexts.
