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Moment-based multivariate permutation tests for ordinal categorical data

机译:基于矩的有序分类数据多元置换检验

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Stochastic dominance problems in testing for ordered categorical variables are of specific interest in performance analysis, because they are frequently encountered in practice and present distinctive difficulties, especially within the framework of likelihood ratio tests. Until now, the literature has essentially considered the univariate case, and several solutions have been proposed to cope with it, most of which are based on restricted maximum likelihood ratio tests. These solutions are generally criticised, because the degree of accuracy of their asymptotic null and alternative distributions is difficult to assess and characterise. In this paper, we propose a new exact solution based on a simultaneous analysis of a finite set of sampling moments of ranks, or general scores, assigned to ordered classes and processed within a permutation approach.
机译:在性能分析中,有序分类变量测试中的随机优势问题特别受关注,因为它们在实践中经常遇到并且存在独特的困难,尤其是在似然比测试的框架内。迄今为止,文献基本上已经考虑了单变量情况,并提出了几种解决方案,其中大多数是基于受限的最大似然比检验。这些解决方案通常受到批评,因为它们的渐近零值和替代分布的准确性程度很难评估和表征。在本文中,我们提出了一种新的精确解决方案,该解决方案是基于同时分析分配给有序类并在置换方法中处理的有限等级的采样矩或总谱矩的同时进行的。

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