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Goodness-of-Fit Tests for Bivariate and Multivariate Skew-Normal Distributions

机译:二元和多元偏正态分布的拟合优度检验

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摘要

Goodness-of-fit tests are proposed for the skew-normal law in arbitrary dimension. In the bivariate case the proposed tests utilize the fact that the moment-generating function of the skew-normal variable is quite simple and satisfies a partial differential equation of the first order. This differential equation is estimated from the sample and the test statistic is constructed as an Li-type distance measure incorporating this estimate. Extension of the procedure to dimension greater than two is suggested whereas an effective bootstrap procedure is used to study the behaviour of the new method with real and simulated data.
机译:针对任意维度的偏态法则,提出了拟合优度检验。在双变量情况下,建议的测试利用了以下事实:偏态正态变量的矩生成函数非常简单,并且满足一阶偏微分方程。该微分方程是从样本中估算出来的,而测试统计量则是结合了这一估算值的李式距离度量。建议将该程序扩展到大于2的维数,而使用有效的自举程序来研究具有实际数据和模拟数据的新方法的行为。

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