论文标题
电子价值作为多重测试中的不均衡权重
E-values as unnormalized weights in multiple testing
论文作者
论文摘要
我们研究了如何组合p值和电子价值，并设计多个测试程序，在这些测试程序中，每个假设都可以使用p值和电子价值。我们的结果为具有数据驱动权重的多次测试提供了新的观点：虽然标准加权多个测试方法需要权重确定性地添加到正在测试的假设的数量中，但我们表明，当权重是独立于p值的电子价值时，不需要这种归一化。可以在荟萃分析设置中获得此类电子价值，其中主要数据集用于计算P值，并且使用独立的辅助数据集来计算电子价值。除了荟萃分析之外，我们展示了可以在单个数据集本身上构建独立的电子价值和p值的设置。我们的程序可能会导致功率大幅增加，尤其是如果非无效假设的电子价值大于一个。
We study how to combine p-values and e-values, and design multiple testing procedures where both p-values and e-values are available for every hypothesis. Our results provide a new perspective on multiple testing with data-driven weights: while standard weighted multiple testing methods require the weights to deterministically add up to the number of hypotheses being tested, we show that this normalization is not required when the weights are e-values that are independent of the p-values. Such e-values can be obtained in the meta-analysis setting wherein a primary dataset is used to compute p-values, and an independent secondary dataset is used to compute e-values. Going beyond meta-analysis, we showcase settings wherein independent e-values and p-values can be constructed on a single dataset itself. Our procedures can result in a substantial increase in power, especially if the non-null hypotheses have e-values much larger than one.
