  We consider deconvolution from repeated observations with unknown error
distribution. So far, this model has mostly been studied under the additional
assumption that the errors are symmetric.
  We construct an estimator for the non-symmetric error case and study its
theoretical properties and practical performance. It is interesting to note
that we can improve substantially upon the rates of convergence which have so
far been presented in the literature and, at the same time, dispose of most of
the extremely restrictive assumptions which have been imposed so far.
