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通过对Bartlett检验统计量的特征函数进行数值反演来计算其精确分布。

Computing the exact distribution of the Bartlett's test statistic by numerical inversion of its characteristic function.

作者信息

Witkovský Viktor

机构信息

Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia.

出版信息

J Appl Stat. 2019 Oct 8;47(13-15):2749-2764. doi: 10.1080/02664763.2019.1675608. eCollection 2020.

Abstract

Application of the exact statistical inference frequently leads to non-standard probability distributions of the considered estimators or test statistics. The exact distributions of many estimators and test statistics can be specified by their characteristic functions, as is the case for the null distribution of the Bartlett's test statistic. However, analytical inversion of the characteristic function, if possible, frequently leads to complicated expressions for computing the distribution function and the corresponding quantiles. An efficient alternative is the well-known method based on numerical inversion of the characteristic functions, which is, however, ignored in popular statistical software packages. In this paper, we present the explicit characteristic function of the corrected Bartlett's test statistic together with the computationally fast and efficient implementation of the approach based on numerical inversion of this characteristic function, suggested for evaluating the exact null distribution used for testing homogeneity of variances in several normal populations, with possibly unequal sample sizes.

摘要

精确统计推断的应用常常导致所考虑的估计量或检验统计量呈现非标准概率分布。许多估计量和检验统计量的精确分布可由其特征函数确定,就像巴特利特检验统计量的零分布那样。然而,特征函数的解析反演(若可行)往往会产生用于计算分布函数和相应分位数的复杂表达式。一种有效的替代方法是基于特征函数数值反演的著名方法,然而,流行的统计软件包中却忽略了这一方法。在本文中,我们给出了修正的巴特利特检验统计量的显式特征函数,以及基于该特征函数数值反演的方法的快速高效实现方式,该方法用于评估在几个正态总体中方差齐性检验的精确零分布,样本量可能不相等。

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