# Consistent Density Deconvolution under Partially Known Error Distribution

We estimate the distribution of a real-valued random variable from contaminated observations. The additive error is supposed to be normally distributed, but with unknown variance. The distribution is identiable from the observations if we restrict the class of considered distributions by a simple… condition in the time domain. A minimum distance estimator is shown to be consistent imposing only a slightly stronger assumption than the identification condition.