Inference via the Skewness-Kurtosis Set

Chris A. J. Klaassen, Bert van Es
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Abstract

Kurtosis minus squared skewness is bounded from below by 1, but for unimodal distributions this parameter is bounded by 189/125. In some applications it is natural to compare distributions by comparing their kurtosis-minus-squared-skewness parameters. The asymptotic behavior of the empirical version of this parameter is studied here for i.i.d. random variables. The result may be used to test the hypothesis of unimodality against the alternative that the kurtosis-minus-squared-skewness parameter is less than 189/125. However, such a test has to be applied with care, since this parameter can take arbitrarily large values, also for multimodal distributions. Numerical results are presented and for three classes of distributions the skewness-kurtosis sets are described in detail.
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通过偏度-峰度集合进行推断
峰度减平方斜度的下界为 1,但对于单模态分布,该参数的下界为 189/125。在某些应用中,通过比较峰度减平方斜度参数来比较分布是很自然的。这里研究的是 i.i.d. 随机变量中该参数经验版本的渐近行为。研究结果可用于检验单模态假设与峰度-减平方-斜度参数小于 189/125 的替代假设。不过,这种检验必须小心谨慎,因为该参数可以任意取大值,多模态分布也是如此。文中给出了数值结果,并详细描述了三类分布的峰度-斜度集。
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