The issue of missing values is an arising difficulty when dealing with paired data. Several test procedures are developed in the literature to tackle this problem. Some of them are even robust under deviations and control type-I error quite accurately. However, most of these methods are not applicable when missing values are present only in a single arm. For this case, we provide asymptotic correct resampling tests that are robust under heteroskedasticity and skewed distributions. The tests are based on a meaningful restructuring of all observed information in quadratic form–type test statistics. An extensive simulation study is conducted exemplifying the tests for finite sample sizes under different missingness mechanisms. In addition, illustrative data examples based on real life studies are analyzed.

中文翻译：

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单臂缺失匹配对的基于渐近的自举方法

在处理配对数据时，缺失值的问题是一个出现的困难。文献中开发了几种测试程序来解决这个问题。其中一些甚至在偏差和控制 I 类错误下都非常准确。然而，当缺失值仅存在于单个臂中时，这些方法中的大多数不适用。对于这种情况，我们提供了在异方差和偏态分布下稳健的渐近正确重采样测试。这些检验基于对二次形式检验统计量中所有观察到的信息进行有意义的重组。进行了广泛的模拟研究，举例说明了不同缺失机制下有限样本量的测试。此外，还分析了基于现实生活研究的说明性数据示例。