  We propose a block-resampling penalization method for marginal density
estimation with nonnecessary independent observations. When the data are
$\beta$ or $\tau$-mixing, the selected estimator satisfies oracle inequalities
with leading constant asymptotically equal to 1. We also prove in this setting
the slope heuristic, which is a data-driven method to optimize the leading
constant in the penalty.
