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The Exact and Near-Exact Distributions of the Likelihood Ratio Statistic for the Block Sphericity Test

机译:块球形测试的似然比统计的精确和近乎精确分布

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Using a suitable decomposition of the null hypothesis of the test of sphericity for k blocks of pi variables, into a sequence of conditionally independent null hypotheses we show that it is possible to obtain the expression of the likelihood ratio test statistic, the expression for the h-th null moment and the characteristic function of the logarithm of the likelihood ratio test statistic. The exact distribution of the logarithm of the likelihood ratio test statistic is then obtained as the distribution of the sum of a Generalized Integer Gamma random variable (r.v.) with the sum of a number of independent Logbeta r.v.'s. This distribution takes the form of a single Generalized Integer Gamma distribution when each set of variables has two variables. In the general case, the development of near-exact distributions arises, from the previous decomposition of the null hypothesis and the consequent induced factorization on the characteristic function, as a natural and practical way to approximate the exact distribution of the test statistic. A measure based on the exact and approximating characteristic functions, which gives an upper bound on the distance between the corresponding distribution functions, is used to assess the quality of the near-exact distributions proposed and to compare them with an asymptotic approximation based on Box's method.
机译:使用合适的分解对PI变量的k块块的球形度的零假设,进入一系列条件独立的零假设,我们表明可以获得似然比测试统计的表达,H的表达式-TH幂矩和似然比测试统计对数的特征函数。然后获得似然比测试统计数的对数的确切分布作为广泛的整数伽马随机变量(R.v.)的分布,其中包含许多独立的logbeta r.v.的总和。当每组变量有两个变量时,该分布采用单个广义整数伽马分布的形式。在一般情况下,出现了近乎精确的分布的发展,从前一个分解的零假设的分解和随之而来的特征函数的诱导分解,作为近似测试统计的确切分布的自然和实用的方法。基于精确且近似特性函数的度量,它在相应的分布函数之间的距离上给出了上限,用于评估所提出的近乎精确分布的质量,并将它们与基于框的方法的渐近近似进行比较。

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