  In a previous article, a least square regression estimation procedure was
proposed: first, we condiser a family of functions and study the properties of
an estimator in every unidimensionnal model defined by one of these functions;
we then show how to aggregate these estimators. The purpose of this paper is to
extend this method to the case of density estimation. We first give a general
overview of the method, adapted to the density estimation problem. We then show
that this leads to adaptative estimators, that means that the estimator reaches
the best possible rate of convergence (up to a $\log$ factor). Finally we show
some ways to improve and generalize the method.
