We consider the problem of estimating a density of probability from indirect data in the spherical convolution model. We aim at building an estimate of the unknown density as a linear combination of functions of an overcomplete dictionary. The procedure is devised through a well-calibrated...

The problem of density estimation in the absence of noise has been widely studied and is well known. However if noise is added to the sample, the procedure must be modified by incorporating a deconvolution. In this paper we do so by using a procedure similar to empirical Bayes estimation which...