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Book Sections Year : 2016

Optimal kernel selection for density estimation

Abstract

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].
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Dates and versions

hal-01224097 , version 1 (06-11-2015)

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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⟩
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