峰度测量:非对称分布不允许?

IF 0.9 4区 数学 Q3 STATISTICS & PROBABILITY Metrika Pub Date : 2024-03-17 DOI:10.1007/s00184-024-00959-z
Andreas Eberl, Bernhard Klar
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引用次数: 0

摘要

偏度和峰度是分布的自然特征。虽然人们早已认识到,与位置和离散度等其他特征相比,它们在本质上更具有纠缠性,但最近 Eberl 和 Klar(Stat Papers 65:415-433, 2024)在研究峰度阶数时更明确地指出了这一点。在本文中,我们分析了这种纠缠对一般峰度度量和几个具体例子的影响。作为一项重要发现,我们证明了以经典阶次方式定义的峰度度量(类似于位置度量、离散度量和偏斜度量)是不存在的。这让人对频繁将此类度量应用于偏斜数据产生了严重怀疑。接下来,我们将考虑有关峰度测量的新旧建议,并评估在哪些附加条件下它们能以有意义的方式测量峰度。与一般情况相比,有些测量方法还能更具体地表示偏度对峰度测量的影响。这对新引入的基于密度的峰度测量系列尤其有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Measures of kurtosis: inadmissible for asymmetric distributions?

Skewness and kurtosis are natural characteristics of a distribution. While it has long been recognized that they are more intrinsically entangled than other characteristics like location and dispersion, this has recently been made more explicit by Eberl and Klar (Stat Papers 65:415–433, 2024) with regard to orders of kurtosis. In this paper, we analyze the implications of this entanglement on kurtosis measures in general and for several specific examples. As a key finding, we show that kurtosis measures that are defined in the classical order-based way, which is analogous to measures of location, dispersion and skewness, do not exist. This raises serious doubts about the frequent application of such measures to skewed data. We then consider old and new proposals for kurtosis measures and evaluate under which additional conditions they measure kurtosis in a meaningful way. Some measures also allow more specific representations of the influence of skewness on the measurement of kurtosis than are available in a general setting. This works particularly well for a family of newly introduced density-based kurtosis measures.

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来源期刊
Metrika
Metrika 数学-统计学与概率论
CiteScore
1.50
自引率
14.30%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Metrika is an international journal for theoretical and applied statistics. Metrika publishes original research papers in the field of mathematical statistics and statistical methods. Great importance is attached to new developments in theoretical statistics, statistical modeling and to actual innovative applicability of the proposed statistical methods and results. Topics of interest include, without being limited to, multivariate analysis, high dimensional statistics and nonparametric statistics; categorical data analysis and latent variable models; reliability, lifetime data analysis and statistics in engineering sciences.
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