  In a circular deconvolution model we consider the fully data driven density
estimation of a circular random variable where the density of the additive
independent measurement error is unknown. We have at hand two independent iid
samples, one of the contaminated version of the variable of interest, and the
other of the additive noise. We show optimality,in an oracle and minimax sense,
of a fully data-driven weighted sum of orthogonal series density estimators.
Two shapes of random weights are considered, one motivated by a Bayesian
approach and the other by a well known model selection method. We derive
non-asymptotic upper bounds for the quadratic risk and the maximal quadratic
risk over Sobolev-like ellipsoids of the fully data-driven estimator. We
compute rates which can be obtained in different configurations for the
smoothness of the density of interest and the error density. The rates
(strictly) match the optimal oracle or minimax rates for a large variety of
cases, and feature otherwise at most a deterioration by a logarithmic factor.
We illustrate the performance of the fully data-driven weighted sum of
orthogonal series estimators by a simulation study.
