圆形数据的两样本非参数检验——它的精确分布和性能。

Sankhya. Series B (2008) Pub Date : 2021-01-01 Epub Date: 2021-02-13 DOI:10.1007/s13571-020-00244-9
S Rao Jammalamadaka, Stéphane Guerrier, Vasudevan Mangalam
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引用次数: 2

摘要

一种非参数测试标记为“Rao间隔频率测试”的探索和开发用于测试两个圆形样本是否来自同一人口。它的确切分布和性能相对于可比较的测试,如惠勒-沃森测试和小样本的狄克逊测试,进行了讨论。尽管这个检验统计量被证明是渐近正态的,正如人们所期望的那样,这个大样本分布并不能为小样本到中等样本提供令人满意的近似。使用组合技术获得了小样本的精确临界值,并提供了表格,并根据这些评估了渐近临界区域。对于中间的中等样本量,即当样本量太大,使得组合技术在计算上令人望而却步,但渐近区域不能提供良好的近似值时,我们提供了一个简单的蒙特卡罗程序,可以给出非常精确的临界值。众所周知,大量通常的基于秩的检验不适用于圆形数据,因为这种秩的值取决于任意选择的原点和所使用的旋转方向(顺时针或逆时针)。在旋转组下不变的测试取决于通过所谓的“间隔频率”获得的数据,即一个样本的频率落在另一个样本的间隔(或间隙)之间。惠勒-沃森、迪克森和提出的Rao检验就是这种形式,对循环数据非常有用,但它们还有一个额外的优点,即对于比较实线上的任何两个样本都是有效和有用的。我们的研究和模拟建立了“Rao间隔频率测试”作为一种理想的,并且确实是在各种情况下比较两个圆形样本的优选测试,并且作为一种可行的竞争对手,即使是真实线上的数据。实现这些测试的计算帮助,可以在网上的“TwoCircles”R包中获得,并且是本文的一部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Two-sample Nonparametric Test for Circular Data- its Exact Distribution and Performance.

A Two-sample Nonparametric Test for Circular Data- its Exact Distribution and Performance.

A Two-sample Nonparametric Test for Circular Data- its Exact Distribution and Performance.

A Two-sample Nonparametric Test for Circular Data- its Exact Distribution and Performance.

A nonparametric test labelled 'Rao Spacing-frequencies test' is explored and developed for testing whether two circular samples come from the same population. Its exact distribution and performance relative to comparable tests such as the Wheeler-Watson test and the Dixon test in small samples, are discussed. Although this test statistic is shown to be asymptotically normal, as one would expect, this large sample distribution does not provide satisfactory approximations for small to moderate samples. Exact critical values for small samples are obtained and tables provided here, using combinatorial techniques, and asymptotic critical regions are assessed against these. For moderate sample sizes in-between i.e. when the samples are too large making combinatorial techniques computationally prohibitive but yet asymptotic regions do not provide a good approximation, we provide a simple Monte Carlo procedure that gives very accurate critical values. As is well-known, the large number of usual rank-based tests are not applicable in the context of circular data since the values of such ranks depend on the arbitrary choice of origin and the sense of rotation used (clockwise or anti-clockwise). Tests that are invariant under the group of rotations, depend on the data through the so-called 'spacing frequencies', the frequencies of one sample that fall in between the spacings (or gaps) made by the other. The Wheeler-Watson, Dixon, and the proposed Rao tests are of this form and are explicitly useful for circular data, but they also have the added advantage of being valid and useful for comparing any two samples on the real line. Our study and simulations establish the 'Rao spacing-frequencies test' as a desirable, and indeed preferable test in a wide variety of contexts for comparing two circular samples, and as a viable competitor even for data on the real line. Computational help for implementing any of these tests, is made available online "TwoCircles" R package and is part of this paper.

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