  We consider the problem of estimating the mixing density $f$ from $n$ i.i.d.
observations distributed according to a mixture density with unknown mixing
distribution. In contrast with finite mixtures models, here the distribution of
the hidden variable is not bounded to a finite set but is spread out over a
given interval. We propose an approach to construct an orthogonal series
estimator of the mixing density $f$ involving Legendre polynomials. The
construction of the orthonormal sequence varies from one mixture model to
another. Minimax upper and lower bounds of the mean integrated squared error
are provided which apply in various contexts. In the specific case of
exponential mixtures, it is shown that the estimator is adaptive over a
collection of specific smoothness classes, more precisely, there exists a
constant $A\textgreater{}0$ such that, when the order $m$ of the projection
estimator verifies $m\sim A \log(n)$, the estimator achieves the minimax rate
over this collection. Other cases are investigated such as Gamma shape mixtures
and scale mixtures of compactly supported densities including Beta mixtures.
Finally, a consistent estimator of the support of the mixing density $f$ is
provided.
