论文标题
关于统计缺陷：为什么匹配方法的测试统计量绝望地且内容丰富
On statistical deficiency: Why the test statistic of the matching method is hopelessly underpowered and uniquely informative
论文作者
论文摘要
随机变化m是组合物质，是比较排列的基础，以及与涉及帽子不当的数百年历史的谜语的解决方案。在统计数据中，M是匹配方法关联的废弃假设统计检验（NHST）的测试统计量。在本文中，我表明匹配方法对其统计功率具有绝对的且相对较低的限制。我首先通过重新诠释Rae的定理来做到这一点，该定理描述了M在真实零下具有几个等级相关统计的M的联合分布。然后，我仅从M的无条件采样分布中得出该属性，在该分布的基础上，我将概念概念：统计量不足以且不一致且相对于其参数效率低下。最后，我演示了M的应用程序，该应用程序利用其缺乏症来合格在共同估计的样本相关性中采样误差。
The random variate m is, in combinatorics, a basis for comparing permutations, as well as the solution to a centuries-old riddle involving the mishandling of hats. In statistics, m is the test statistic for a disused null hypothesis statistical test (NHST) of association, the matching method. In this paper, I show that the matching method has an absolute and relatively low limit on its statistical power. I do so first by reinterpreting Rae's theorem, which describes the joint distributions of m with several rank correlation statistics under a true null. I then derive this property solely from m's unconditional sampling distribution, on which basis I develop the concept of a deficient statistic: a statistic that is insufficient and inconsistent and inefficient with respect to its parameter. Finally, I demonstrate an application for m that makes use of its deficiency to qualify the sampling error in a jointly estimated sample correlation.
