Optimal kernel selection for density estimation

Matthieu Lerasle (1) , Nelo Magalhães (2) , Patricia Reynaud-Bouret (3)

1 LJAD - Laboratoire Jean Alexandre Dieudonné
2 UPMC - Université Pierre et Marie Curie - Paris 6
3 JAD - Laboratoire Jean Alexandre Dieudonné

We provide new general kernel selection rules thanks to penalized least-squares criteria. We derive optimal oracle inequalities using adequate concentration tools. We also investigate the problem of minimal penalty as described in [BM07].

Mots clés: minimal penalty; optimal penalty; oracle inequalities; kernel estimators; density estimation

Domaines: Statistiques [math.ST]

Matthieu Lerasle, Nelo Magalhães, Patricia Reynaud-Bouret. Optimal kernel selection for density estimation. High Dimensional Probability VII : The Cargese Volume, 71, Birkhauser, pp.425-460, 2016, Prog. Probab, 978-3-319-40519-3_19. ⟨10.1007/978-3-319-40519-3_19⟩. ⟨hal-01224097⟩