论文标题
阶乘设计中多元变化系数的排列测试
Permutation test for the multivariate coefficient of variation in factorial designs
论文作者
论文摘要
提出了多元变异系数及其倒数系数的新推理方法，标准化平均值。尽管在单变量的情况下有两个参数的测试程序，但在多变量设置中如何进行推断是鲜为人知的。有一些现有的程序，但它们依赖于对基础分布的限制性假设。我们通过在一般，可能异质的阶乘设计的背景下应用WALD型统计数据来解决这个问题。除了$ k $样本的情况外，还可以将高速公路布局纳入此框架中，从而可以讨论主要和交互作用。在零假设下，所得过程显示出渐近有效，并且在一般替代方案下是一致的。为了提高有限样本性能，我们建议测试的置换版本，并表明可以将测试的渐近性能传递给它们。一项详尽的模拟研究比较了新测试，它们的排列对应物和现有方法。为了进一步分析测试之间的差异，我们进行了两个说明性的真实数据示例。
New inference methods for the multivariate coefficient of variation and its reciprocal, the standardized mean, are presented. While there are various testing procedures for both parameters in the univariate case, it is less known how to do inference in the multivariate setting appropriately. There are some existing procedures but they rely on restrictive assumptions on the underlying distributions. We tackle this problem by applying Wald-type statistics in the context of general, potentially heteroscedastic factorial designs. In addition to the $k$-sample case, higher-way layouts can be incorporated into this framework allowing the discussion of main and interaction effects. The resulting procedures are shown to be asymptotically valid under the null hypothesis and consistent under general alternatives. To improve the finite sample performance, we suggest permutation versions of the tests and shown that the tests' asymptotic properties can be transferred to them. An exhaustive simulation study compares the new tests, their permutation counterparts and existing methods. To further analyse the differences between the tests, we conduct two illustrative real data examples.
