Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/100146
Title: Kernel density estimation for circular data: a Fourier series-based plug-in approach for bandwidth selection
Authors: Tenreiro, Carlos 
Keywords: Circular data; Kernel density estimation; Bandwidth selection; Plug-in rule; Fourier series-based estimators
Issue Date: 2022
Publisher: Taylor and Francis
Project: Centre for Mathematics of the University of Coimbra - UIDB/00324/2020 
Serial title, monograph or event: Journal of Nonparametric Statistics
Volume: 34 (2)
Abstract: In this paper we derive asymptotic expressions for the mean integrated squared error of a class of delta sequence density estimators for circular data. This class includes the class of kernel density estimators usually considered in the literature, as well as a new class which is closer in spirit to the class of Parzen--Rosenblatt estimators for linear data. For these two classes of kernel density estimators, a Fourier series-based direct plug-in approach for bandwidth selection is presented. The proposed bandwidth selector has a $n^{-1/2}$ relative convergence rate whenever the underlying density is smooth enough and the simulation results testify that it presents a very good finite sample performance against other bandwidth selectors in the literature.
URI: http://hdl.handle.net/10316/100146
DOI: 10.1080/10485252.2022.2057974
Rights: embargoedAccess
Appears in Collections:I&D CMUC - Artigos em Revistas Internacionais

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