Optimal model selection for stationary data under various mixing conditions

We build penalized least-squares estimators of the marginal density of a stationary process, using the slope algorithm and resampling penalties. When the data are $\beta$ or $\tau$-mixing, these estimators satisfy oracle inequalities with leading constant asymptotically equal to 1.