论文标题
可靠的基于数据的平滑参数选择方法，用于循环内核估计
A reliable data-based smoothing parameter selection method for circular kernel estimation
论文作者
论文摘要
提出了一个新的基于数据的平滑参数（及其导数）估计。遵循插件的想法，最佳平滑参数上的未知数量被合适的估计取代。本文提供了众所周知的Sheather和Jones带宽的圆形版本（DOI：10.1111/j.2517-6161.1991.tb01857.x），并具有直接和求解方程式插件规则。提供了对我们的发展的理论支持，与密度的估计值的渐近平方平方误差有关，其衍生物及其功能以及圆形数据的功能。将所提出的选择器与以前的基于数据的平滑参数进行比较，以进行循环内核密度估计。本文还有助于研究圆形数据的最佳内核。还使用有关汽车事故时间的真实数据显示了建议的插件规则的说明。
A new data-based smoothing parameter for circular kernel density (and its derivatives) estimation is proposed. Following the plug-in ideas, unknown quantities on an optimal smoothing parameter are replaced by suitable estimates. This paper provides a circular version of the well-known Sheather and Jones bandwidths (DOI: 10.1111/j.2517-6161.1991.tb01857.x), with direct and solve-the-equation plug-in rules. Theoretical support for our developments, related to the asymptotic mean squared error of the estimator of the density, its derivatives, and its functionals, for circular data, are provided. The proposed selectors are compared with previous data-based smoothing parameters for circular kernel density estimation. This paper also contributes to the study of the optimal kernel for circular data. An illustration of the proposed plug-in rules is also shown using real data on the time of car accidents.
