SIMULTANEOUS INFERENCE: WHEN SHOULD HYPOTHESIS TESTING PROBLEMS BE COMBINED?
SIMULTANEOUS INFERENCE: WHEN SHOULD HYPOTHESIS TESTING PROBLEMS BE COMBINED?
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DOI：
10.1214/07-aoas141
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发表时间：
2008-03-01
影响因子：
1.8
通讯作者：
Efron, Bradley
中科院分区：
文献类型：
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作者：
Efron, Bradley
现代统计学家经常同时面临数百或数千个假设检验问题，这些问题来自新的科学技术，如微阵列，医疗和卫星成像设备或流式细胞仪计数器。相关的统计学文献倾向于开始假设一个单一的组合分析，例如，错误发现率评估，应适用于手头的整个问题集。这可能是一个危险的假设。正如本文中的例子所示，在任何特定的情况下，导致过于保守或过于自由的结论。一个简单的贝叶斯理论产生了一个简洁的描述的效果，分离或组合石油假发现率分析。该理论允许在小的子类中进行有效的测试。并可应用于“富集”，即检测多病例效应。
Modern statisticians are often presented with hundreds or thousands of hypothesis testing problems to evaluate at the same time, generated from new scientific technologies such as microarrays, medical and satellite imaging devices, or flow cytometry counters. The relevant statistical literature tend, to begin with the tacit assumption that a single combined analysis, for instance, a False Discovery Rate assessment, should be applied to the entire set of problems at hand. This can be a dangerous assumption. as the examples in the paper show, leading to overly conservative or overly liberal conclusions within any particular subclass of the cases. A simple Bayesian theory yields a succinct description of the effects of separation or combination oil false discovery rate analyses. The theory allows efficient testing within small subclasses. and has applications to "enrichment," the detection of multi-case effects.