Selective inference on multiple families of hypotheses
Selective inference on multiple families of hypotheses
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DOI：
10.1111/rssb.12028
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发表时间：
2014-01-01
影响因子：
5.8
通讯作者：
Bogomolov, Marina
中科院分区：
文献类型：
--
作者：
Benjamini, Yoav;Bogomolov, Marina
在许多复杂的多重检验问题中，假设被分成若干族。根据这些数据，选择具有真实发现证据的家族，并对其中的假设进行测试。无论是单独控制每个家族中的错误率，还是一起控制所有假设的错误率，都不能确保在选定家族中过滤错误的一定程度的置信度。我们制定这种关注的选择性推理的一般性，对于一个非常广泛的类的错误率和任何选择标准，并提出了一个调整的测试水平内的选定的家庭，保留控制的预期平均误差超过选定的家庭。
In many complex multiple-testing problems the hypotheses are divided into families. Given the data, families with evidence for true discoveries are selected, and hypotheses within them are tested. Neither controlling the error rate in each family separately nor controlling the error rate over all hypotheses together can assure some level of confidence about the filtration of errors within the selected families. We formulate this concern about selective inference in its generality, for a very wide class of error rates and for any selection criterion, and present an adjustment of the testing level inside the selected families that retains control of the expected average error over the selected families.