  In this paper we study the aggregation problem that can be formulated as
follows. Assume that we have a family of estimators $\mathcal{F}$ built on the
basis of available observations. The goal is to construct a new estimator whose
risk is as close as possible to that of the best estimator in the family. We
propose a general aggregation scheme that is universal in the following sense:
it applies for families of arbitrary estimators and a wide variety of models
and global risk measures. The procedure is based on comparison of empirical
estimates of certain linear functionals with estimates induced by the family
$\mathcal{F}$. We derive oracle inequalities and show that they are
unimprovable in some sense. Numerical results demonstrate good practical
behavior of the procedure.
