  We observe a random measure $N$ and aim at estimating its intensity $s$. This
statistical framework allows to deal simultaneously with the problems of
estimating a density, the marginals of a multivariate distribution, the mean of
a random vector with nonnegative components and the intensity of a Poisson
process. Our estimation strategy is based on estimator selection. Given a
family of estimators of $s$ based on the observation of $N$, we propose a
selection rule, based on $N$ as well, in view of selecting among these. Little
assumption is made on the collection of estimators. The procedure offers the
possibility to perform model selection and also to select among estimators
associated to different model selection strategies. Besides, it provides an
alternative to the $T$-estimators as studied recently in Birg\'e (2006). For
illustration, we consider the problems of estimation and (complete) variable
selection in various regression settings.
