Adaptive optimal kernel density estimation for directional data

Thanh Mai Pham Ngoc

Journal of Multivariate Analysis, 2019, vol. 173, issue C, 248-267

Abstract: This paper considers nonparametric density estimation with directional data. 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.

Keywords: Bandwidth selection; Directional data; Kernel density estimation; Oracle inequality; Penalization methods (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.jmva.2019.02.009

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