  Let $Y\in\R^n$ be a random vector with mean $s$ and covariance matrix
$\sigma^2P_n\tra{P_n}$ where $P_n$ is some known $n\times n$-matrix. We
construct a statistical procedure to estimate $s$ as well as under moment
condition on $Y$ or Gaussian hypothesis. Both cases are developed for known or
unknown $\sigma^2$. Our approach is free from any prior assumption on $s$ and
is based on non-asymptotic model selection methods. Given some linear spaces
collection $\{S_m,\ m\in\M\}$, we consider, for any $m\in\M$, the least-squares
estimator $\hat{s}_m$ of $s$ in $S_m$. Considering a penalty function that is
not linear in the dimensions of the $S_m$'s, we select some $\hat{m}\in\M$ in
order to get an estimator $\hat{s}_{\hat{m}}$ with a quadratic risk as close as
possible to the minimal one among the risks of the $\hat{s}_m$'s.
Non-asymptotic oracle-type inequalities and minimax convergence rates are
proved for $\hat{s}_{\hat{m}}$. A special attention is given to the estimation
of a non-parametric component in additive models. Finally, we carry out a
simulation study in order to illustrate the performances of our estimators in
practice.
