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Testing Simultaneous Marginal Homogeneity for Clustered Matched-Pair Multinomial Data

机译:测试聚类匹配对多项数据的同时边缘均匀性

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For matched-pair data with a polychotomous outcome, the Stuart-Maxwell test (1955) and the Bhapkar test(1966) are commonly used to test marginal homogeneity. When the outcome is ordinal, the test proposed by Agresti (1983) can be used to test the marginal homogeneity against stochastic order. In practice, we often face the need to consider multiple categorical outcomes simultaneously to insure Type I error protection. In this paper, we propose three statistics to test simultaneous marginal homogeneity for multiple multinomial outcomes in two dependent samples. Furthermore, when the outcome is ordinal, we also propose a transformed version of the three statistics for testing simultaneous marginal homogeneity against stochastic order in two dependent samples. We then prove their asymptotic properties. Finally, Monte Carlo simulations are conducted to evaluate their performance in small samples with respect to empirical size and power.
机译:对于具有聚奇异结果的匹配对数据,斯图尔特 - 麦克斯韦尔测试(1955)和Bhapkar测试(1966)通常用于测试边缘均匀性。 当结果是序数时,Agresti(1983)提出的测试可用于测试对随机顺序的边际均匀性。 在实践中,我们经常面临同时考虑多种分类结果的必要性,以确保I型错误保护。 在本文中,我们提出了三种统计数据来测试两种依赖样本中多项多项式结果的同时边缘均匀性。 此外,当结果是序数时,我们还提出了三个统计数据的转化版本,用于在两个依赖样本中对随机顺序进行同时边缘均匀性进行测试。 然后我们证明了他们的渐近性质。 最后,进行了蒙特卡罗模拟,以评估其在小型样本中的性能相对于经验尺寸和功率。

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