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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: | 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: | https://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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