Kernel density estimation for circular data: a Fourier series-based plug-in approach for bandwidth selection

Type: Article

Publication Date: 2022-04-03

Citations: 9

DOI: https://doi.org/10.1080/10485252.2022.2057974

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 that 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.

Locations

  • Journal of nonparametric statistics - View
  • Estudo Geral (Universidad de Coimbra) - View - PDF
  • INDIGO (University of Illinois at Chicago) - View - PDF

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