Adaptive optimal kernel density estimation for directional data

被引：7
|
作者：
Thanh Mai Pham Ngoc [1 ]
机构：
[1] Univ Paris Saclay, Univ Paris Sud, CNRS, Lab Math Orsay, F-91405 Orsay, France
来源：
JOURNAL OF MULTIVARIATE ANALYSIS | 2019年 / 173卷
关键词：
Bandwidth selection; Directional data; Kernel density estimation; Oracle inequality; Penalization methods; BANDWIDTH SELECTION; RULE;
D O I：
10.1016/j.jmva.2019.02.009
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
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.
引用
收藏
页码：248 / 267
页数：20
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