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Testing the homogeneity of proportions for clustered binary data without knowing the correlation structure

机译:在不知道相关结构的情况下测试聚类二进制数据的比例均匀性

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摘要

A robust generalized score test for comparing groups of cluster binary data is proposed. This novel test is asymptotically valid for practically any underlying correlation configurations including the situation when correlation coefficients vary within or between clusters. This structure generally undermines the validity of the typical large sample properties of the method of maximum likelihood. Simulations and real data analysis are used to demonstrate the merit of this parametric robust method. Results show that our test is superior to two recently proposed test statistics advocated by other researchers.
机译:提出了一种鲁棒的广义得分测试,用于比较集群二进制数据的组。对于所有潜在的相关配置,包括相关系数在群集内或群集之间变化的情况,这种新颖的测试在渐近有效。这种结构通常会破坏最大似然方法的典型大样本属性的有效性。仿真和真实数据分析用于证明该参数鲁棒方法的优点。结果表明,我们的测试优于其他研究人员最近提出的两项测试统计数据。

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