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Title: Adaptive optimal kernel density estimation for directional data Authors:  Thanh Mai Pham Ngoc - University Paris Sud Orsay (France) [presenting]
Abstract: Nonparametric density estimation with directional data is considered. A new rule is proposed for bandwidth selection for kernel density estimation. The procedure is automatic, fully data-driven, and adaptive to the degree of smoothness of the density. An oracle inequality and optimal rates of convergence for the L2 error are derived. These theoretical results are illustrated with simulations.