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On some validity-robust tests for the homogeneity of concentrations on spheres

机译:关于球体上浓度均一性的一些稳健性检验

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In this paper we tackle the problem of testing the homogeneity of concentrations for directional data. All the existing procedures for this problem are parametric procedures based on the assumption of a Fisher-von Mises-Langevin (FvML) distribution. We construct here a pseudo-FvML test and a rank-based Kruskal-Wallis-type test for this problem. The pseudo-FvML test improves on the traditional FvML parametric procedures by being asymptotically valid under the whole semiparametric class of rotationally symmetric distributions. Furthermore, it is asymptotically equivalent to the locally and asymptotically most stringent parametric FvML procedure in the FvML case. The Kruskal-Wallis rank-based test is also asymptotically valid under rotationally symmetric distributions and performs nicely under various important distributions. The finite-sample behaviour of the proposed tests is investigated by means of a Monte Carlo simulation.
机译:在本文中,我们解决了针对方向数据测试浓度均匀性的问题。基于该问题的所有现有过程都是基于Fisher-von Mises-Langevin(FvML)分布的假设的参数过程。在此,我们针对此问题构建了伪FvML测试和基于等级的Kruskal-Wallis型测试。伪FvML测试通过在旋转对称分布的整个半参数类别下渐近有效,对传统FvML参数过程进行了改进。此外,在FvML情况下,它渐近等效于局部且渐近最严格的参数FvML过程。基于Kruskal-Wallis等级的检验在旋转对称分布下也渐近有效,并且在各种重要分布下表现良好。通过蒙特卡洛模拟研究了所提出测试的有限样本行为。

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