自适应符号误差控制
Adaptive Sign Error Control
摘要：
在多重检验场景中，通常当参数的估计值在绝对值上超过某个显著性阈值时，推断参数的符号。典型地，选择显著性阈值以控制按实验的第一类错误率、按族的第一类错误率或错误发现率。然而，控制这些错误率并不能显式地控制符号错误率。在本文中，我们提出了两个程序，自适应地选择一个experimentwise显著性阈值，以控制符号错误率。第一个控制符号误差率保守，没有任何分布假设的参数的兴趣。第二个是经验贝叶斯过程，当正确指定参数分布模型时，渐进地实现最佳性能。我们还讨论了一个自适应程序，以尽量减少符号错误率时，experimentwise第一类错误率是固定的。
英文摘要：
In multiple testing scenarios, typically the sign of a parameter is inferred when its estimate exceeds some significance threshold in absolute value. Typically, the significance threshold is chosen to control the experimentwise type I error rate, family-wise type I error rate or the false discovery rate. However, controlling these error rates does not explicitly control the sign error rate. In this paper, we propose two procedures for adaptively selecting an experimentwise significance threshold in order to control the sign error rate. The first controls the sign error rate conservatively, without any distributional assumptions on the parameters of interest. The second is an empirical Bayes procedure, and achieves optimal performance asymptotically when a model for the distribution of the parameters is correctly specified. We also discuss an adaptive procedure to minimize the sign error rate when the experimentwise type I error rate is held fixed.
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。
