Apparaît dans la collection : 2022 - T3 - WS1 - Non-linear and high dimensional inference

In this talk we consider theproblem of estimator selection. In the case of density estimation, we study a method called PCO, which is intermediate between Lepski’s method and penalized empirical risk minimization. The key point is the comparison of all the estimators to the overfitted one. We provide some theoretical results which lead to some fully data-driven selection strategy. We will also show the numerical performance of the method. (Joint work with P. Massart, V. Rivoirard and S. Varet.)