  In this paper we study the problem of adaptive estimation of a multivariate
function satisfying some structural assumption. We propose a novel estimation
procedure that adapts simultaneously to unknown structure and smoothness of the
underlying function. The problem of structural adaptation is stated as the
problem of selection from a given collection of estimators. We develop a
general selection rule and establish for it global oracle inequalities under
arbitrary $\rL_p$--losses. These results are applied for adaptive estimation in
the additive multi--index model.
