Adaptive optimal kernel density estimation for directional data

JOURNAL OF MULTIVARIATE ANALYSIS

卷:173

Article

Thanh Mai Pham Ngoc¹ 

[1] Univ Paris Saclay, Univ Paris Sud, CNRS, Lab Math Orsay, F-91405 Orsay, France

关键词: Bandwidth selection;    Directional data;    Kernel density estimation;    Oracle inequality;    Penalization methods;   

DOI  :  10.1016/j.jmva.2019.02.009

来源: Elsevier

【 摘 要 】

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 L-2 error are derived. These theoretical results are illustrated with simulations. (C) 2019 Elsevier Inc. All rights reserved.

【 授权许可】

Free