Optimal model selection for stationary data under various mixing conditions
Résumé
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 $1$.
| Origine | Fichiers produits par l'(les) auteur(s) |
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