标题： 同时推断：何时应将假设检验问题结合起来？ 显示英文标题

标题： Simultaneous inference: When should hypothesis testing problems be combined?

摘要： 现代统计学家经常需要同时评估数百或数千个假设检验问题，这些问题是从新的科学技术生成的，例如微阵列、医学和卫星成像设备，或者流式细胞计数器。 相关统计文献往往以一种隐含的假设开始，即应该对当前的所有问题集应用单一的综合分析，例如，错误发现率评估。 这可能是一个危险的假设，正如论文中的例子所示，会导致在任何特定子类别的案例中得出过于保守或过于宽松的结论。 一个简单的贝叶斯理论可以简洁地描述分离或组合对错误发现率分析的影响。 该理论允许在小型子类别中进行高效的检验，并应用于“富集”，即检测多案例效应。 显示英文摘要

摘要： 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 tends 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 on false discovery rate analyses. The theory allows efficient testing within small subclasses, and has applications to ``enrichment,'' the detection of multi-case effects.