交互式去掩蔽的全局错误率控制
Familywise Error Rate Control by Interactive Unmasking
摘要：
我们提出了一种方法，多个假设检验与familywise错误率（FWER）控制，称为i-FWER测试。大多数测试方法都是预定义的算法，不允许在观察数据后进行修改。然而，在实践中，分析师倾向于在观察数据后选择有前途的算法;不幸的是，这违反了结论的有效性。i-FWER测试具有很大的灵活性：人类（或代表人类行事的计算机程序）可以以数据依赖的方式自适应地引导算法。我们证明，如果分析人员遵守特定的“掩蔽”和“解蔽”方案，则我们的检测质控品FWER。我们通过数值实验证明了我们的测试结构化的非空值下的权力，然后探索新形式的掩蔽。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
