标题： 排列不完整配对数据：一种新颖的精确和渐近正确的随机化检验 显示英文标题

标题： Permuting Incomplete Paired Data: A Novel Exact and Asymptotic Correct Randomization Test

摘要： 针对存在缺失值的配对数据均值相等性检验，已经开发了各种统计检验方法。 然而，大多数现有方法通常基于某些分布假设，例如正态性、0-对称性或数据的同方差性。 本文的目的是开发一种统计检验方法，该方法对这些假设的偏离具有稳健性，并且在异方差性或偏斜分布情况下也能得出有效的推断。 这是通过应用一种新颖的随机化方法实现的。 该检验过程不仅被证明是渐近正确的，而且如果数据的分布相对于所考虑的随机化组是不变的，则在有限样本下也是精确的。 此外，在一项广泛的模拟研究中进一步研究了其小样本性能，并与现有方法进行了比较。 最后，分析了一个示例数据集。 显示英文摘要

摘要： Various statistical tests have been developed for testing the equality of means in matched pairs with missing values. However, most existing methods are commonly based on certain distributional assumptions such as normality, 0-symmetry or homoscedasticity of the data. The aim of this paper is to develop a statistical test that is robust against deviations from such assumptions and also leads to valid inference in case of heteroscedasticity or skewed distributions. This is achieved by applying a novel randomization approach. The resulting test procedure is not only shown to be asymptotically correct but is also finitely exact if the distribution of the data is invariant with respect to the considered randomization group. Its small sample performance is further studied in an extensive simulation study and compared to existing methods. Finally, an illustrative data example is analyzed.