  We consider the classical problem of estimating a vector
$\bolds{\mu}=(\mu_1,...,\mu_n)$ based on independent observations $Y_i\sim
N(\mu_i,1)$, $i=1,...,n$. Suppose $\mu_i$, $i=1,...,n$ are independent
realizations from a completely unknown $G$. We suggest an easily computed
estimator $\hat{\bolds{\mu}}$, such that the ratio of its risk
$E(\hat{\bolds{\mu}}-\bolds{\mu})^2$ with that of the Bayes procedure
approaches 1. A related compound decision result is also obtained. Our
asymptotics is of a triangular array; that is, we allow the distribution $G$ to
depend on $n$. Thus, our theoretical asymptotic results are also meaningful in
situations where the vector $\bolds{\mu}$ is sparse and the proportion of zero
coordinates approaches 1. We demonstrate the performance of our estimator in
simulations, emphasizing sparse setups. In ``moderately-sparse'' situations,
our procedure performs very well compared to known procedures tailored for
sparse setups. It also adapts well to nonsparse situations.
