论文标题
通过交互式删除来控制家庭错误率
Familywise Error Rate Control by Interactive Unmasking
论文作者
论文摘要
我们提出了一种使用族误差率（FWER）控制的多种假设检验的方法，称为I-FWER检验。大多数测试方法是预定义的算法，在观察数据后不允许修改。但是，实际上，分析师在观察数据后倾向于选择有希望的算法。不幸的是，这违反了结论的有效性。 I-FWER测试允许极大的灵活性：人类（或代表人类作用的计算机程序）可以以数据依赖性方式自适应地指导该算法。如果分析师遵守“掩盖”和“卸载”的特定协议，我们证明我们的测试控制FWER。我们通过数值实验在结构化的非腔内进行测试的功能，然后探索新形式的掩盖形式。
We propose a method for multiple hypothesis testing with familywise error rate (FWER) control, called the i-FWER test. Most testing methods are predefined algorithms that do not allow modifications after observing the data. However, in practice, analysts tend to choose a promising algorithm after observing the data; unfortunately, this violates the validity of the conclusion. The i-FWER test allows much flexibility: a human (or a computer program acting on the human's behalf) may adaptively guide the algorithm in a data-dependent manner. We prove that our test controls FWER if the analysts adhere to a particular protocol of "masking" and "unmasking". We demonstrate via numerical experiments the power of our test under structured non-nulls, and then explore new forms of masking.
