Nonparametric estimation of directional highest density regions
Nonparametric estimation of directional highest density regions
复制标题
方向最高密度区域的非参数估计
DOI：
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复制
发表时间：
2020
影响因子：
1.6
通讯作者：
R. Crujeiras
中科院分区：
文献类型：
--
作者：
P. Saavedra;R. Crujeiras
最高密度区域（hdr）被定义为包含相对高密度样本点的水平集。虽然统计文献中已经广泛考虑了从底层密度生成的随机样本中进行欧几里得HDR估计，但尚未考虑定向数据的这个问题。在这项工作中，正式定义了定向hdr，并提出了基于核平滑和相关置信区域的插件估计器。我们还为插件hdr估计提供了一种新的合适的自举带宽选择器，该选择器基于最小化误差标准，该标准涉及理论和估计hdr边界之间的豪斯多夫距离。广泛的仿真研究表明了所得到的估计器对圆和球的性能。将该方法应用于动物定位和地震学两个实际数据集的分析。
Highest density regions (HDRs) are defined as level sets containing sample points of relatively high density. Although Euclidean HDR estimation from a random sample, generated from the underlying density, has been widely considered in the statistical literature, this problem has not been contemplated for directional data yet. In this work, directional HDRs are formally defined and plug-in estimators based on kernel smoothing and associated confidence regions are proposed. We also provide a new suitable bootstrap bandwidth selector for plug-in HDRs estimation based on the minimization of an error criteria that involves the Hausdorff distance between the boundaries of the theoretical and estimated HDRs. An extensive simulation study shows the performance of the resulting estimator for the circle and for the sphere. The methodology is applied to analyze two real data sets in animal orientation and seismology.
影响因子：
1.1
作者：
Doss, Charles R.;Weng, Guangwei
通讯作者：
Weng, Guangwei
完全自适应的基于密度的聚类
DOI：
10.1214/15-aos1331
发表时间：
2015
期刊：
arXiv: Methodology
影响因子：
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作者：
I. Steinwart
通讯作者：
I. Steinwart