Abstract：
Closed testing has recently been shown to be optimal for simultaneous true discovery proportion control. It is, however, challenging to construct true discovery guarantee procedures in such a way that it focuses power on some feature sets chosen by users based on their specific interest or expertise. We propose a procedure that allows users to target power on prespecified feature sets, that is, \"focus sets.\" Still, the method also allows inference for feature sets chosen post hoc, that is, \"nonfocus sets,\" for which we deduce a true discovery lower confidence bound by interpolation. Our procedure is built from partial true discovery guarantee procedures combined with Holm\'s procedure and is a conservative shortcut to the closed testing procedure. A simulation study confirms that the statistical power of our method is relatively high for focus sets, at the cost of power for nonfocus sets, as desired. In addition, we investigate its power property for sets with specific structures, for example, trees and directed acyclic graphs. We also compare our method with AdaFilter in the context of replicability analysis. The application of our method is illustrated with a gene ontology analysis in gene expression data.
摘要：
最近已证明封闭测试对于同时进行真实发现比例控制是最佳的。是的,然而,构建真正的发现保证程序具有挑战性，因为它将权力集中在用户根据他们的特定兴趣或专业知识选择的某些特征集上。我们提出了一个程序，允许用户以预定的功能集为目标电源，也就是说,\"焦点集。“尽管如此，该方法还允许推断事后选择的特征集，也就是说,\"非焦点集，\“为此，我们推导了一个由插值限制的真正的发现较低的置信度。我们的程序是由部分真实发现保证程序与Holm\的程序相结合而构建的，是封闭测试程序的保守捷径。仿真研究证实，对于焦点集，我们方法的统计能力相对较高，以非聚焦集的功率为代价，根据需要。此外,我们研究了具有特定结构的集合的功率属性，例如,树和有向无环图。我们还在可复制性分析的背景下将我们的方法与AdaFilter进行了比较。通过基因本体分析在基因表达数据中说明了我们方法的应用。
