Paper Info
Title
Adaptive optimal kernel density estimation for directional data.
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.
Year
DOI
Venue
2019
10.1016/j.jmva.2019.02.009
Journal of Multivariate Analysis
Keywords
Field
DocType
primary
Convergence (routing),Applied mathematics,Oracle inequality,Bandwidth (signal processing),Smoothness,Statistics,Nonparametric density estimation,Mathematics,Kernel density estimation
Journal
Volume
ISSN
Citations 
173
0047-259X
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Thanh Mai Pham Ngoc100.68