Séminaire MODAL'X : Vincent Rivoirard (CEREMADE, Université Paris Dauphine)

Résumé : Kernel density estimation is a well known method involving a smoothing parameter (the bandwidth) that needs to be tuned by the user. Although this method has been widely used the bandwidth selection remains a challenging issue in terms of balancing algorithmic performance and statistical relevance. The purpose of this talk is to present a new method for bandwidth selection. This new method is called Penalized Comparison to Overfitting (PCO). We provide some theoretical results which lead to some fully data-driven selection strategy. It is compared to other usual bandwidth selection methods for univariate and also multivariate kernel density estimation.