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Density deconvolution under general assumptions on the distribution of measurement errors
In this paper we study the problem
of density deconvolution under general assumptions on the measurement error distribution. Typically
deconvolution estimators are constructed using Fourier transform techniques, and
it is assumed that
the characteristic function of
the measurement errors does not have zeros
on the real line. This assumption is rather strong and is not fulfilled
in many cases of interest. In this paper we develop a
methodology for constructing optimal density deconvolution estimators in the general setting that covers
vanishing and non--vanishing characteristic functions of the measurement errors.
We derive upper bounds on the risk of the proposed estimators and
provide sufficient conditions under which zeros of the corresponding characteristic function have no effect on estimation accuracy.
Moreover, we show that the derived conditions are also necessary in some
specific problem instances.