Kernel density estimation for circular data: a Fourier series-based plug-in approach for bandwidth selection
Kernel density estimation for circular data: a Fourier series-based plug-in approach for bandwidth selection
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循环数据的核密度估计：基于傅立叶级数的带宽选择插件方法
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Carlos Tenreiro
中科院分区：
文献类型：
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作者：
Carlos Tenreiro
本文给出了圆形数据下一类δ序列密度估计的积分均方误差的渐近表达式。这个类包括核密度估计类通常在文献中考虑，以及一个新的类，在精神上更接近类的Parzen-Rosenblatt估计线性数据。对于这两类核密度估计，提出了一种基于傅立叶级数的直接插入式带宽选择方法。当底层密度足够光滑时，所提出的带宽选择器具有相对收敛速度，仿真结果表明，与文献中的其他带宽选择器相比，它具有非常好的有限样本性能。
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 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.