论文标题
Stein拟合方向分布的拟合优度测试
A Stein Goodness-of-fit Test for Directional Distributions
论文作者
论文摘要
在许多领域中，数据以方向（单位向量）的形式出现，通常的统计过程不适用于此类方向数据。在这项研究中，我们提出了基于内核Stein差异的一般方向分布的非参数拟合测试程序。我们的方法基于Stein在球体上的操作员，该操作员是通过使用Stokes定理得出的。值得注意的是，所提出的方法适用于具有棘手的归一化常数的分布，通常出现在方向统计中。实验结果表明，所提出的方法可以很好地控制I型误差，并且比现有测试具有更大的功率，包括基于最大平均差异的测试。
In many fields, data appears in the form of direction (unit vector) and usual statistical procedures are not applicable to such directional data. In this study, we propose non-parametric goodness-of-fit testing procedures for general directional distributions based on kernel Stein discrepancy. Our method is based on Stein's operator on spheres, which is derived by using Stokes' theorem. Notably, the proposed method is applicable to distributions with an intractable normalization constant, which commonly appear in directional statistics. Experimental results demonstrate that the proposed methods control type-I error well and have larger power than existing tests, including the test based on the maximum mean discrepancy.
