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

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Detalhes bibliográficos
Resumo: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.
Autores principais:Tenreiro, Carlos
Assunto:Circular data Kernel density estimation Bandwidth selection Plug-in rule Fourier series-based estimators
Ano:2022
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso embargado
Instituição associada:Universidade de Coimbra
Idioma:inglês
Origem:Estudo Geral - Universidade de Coimbra
Descrição
Resumo: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.