论文标题
同时通过仿制和封闭测试同时进行虚假发现比例范围
Simultaneous false discovery proportion bounds via knockoffs and closed testing
论文作者
论文摘要
我们提出了新的方法，以获得基于仿基的方法的同时错误发现比例。我们首先研究了一种基于Janson和SU的$ K $ -FAMILYWISE错误率控制方法和插值的方法。然后，我们通过考虑$ K $值的集合来概括它，并表明Katsevich和Ramdas的界限是这种方法的特殊情况，并且可以均匀改进。接下来，我们通过使用多加权和本地测试统计量的封闭测试进一步概括了该方法。这使我们能够获得对先前方法的进一步统一改进和其他概括。我们还为其实施开发了有效的快捷方式。我们比较了我们在模拟中提出的方法的性能，并将其应用于英国生物库的数据集。
We propose new methods to obtain simultaneous false discovery proportion bounds for knockoff-based approaches. We first investigate an approach based on Janson and Su's $k$-familywise error rate control method and interpolation. We then generalize it by considering a collection of $k$ values, and show that the bound of Katsevich and Ramdas is a special case of this method and can be uniformly improved. Next, we further generalize the method by using closed testing with a multi-weighted-sum local test statistic. This allows us to obtain a further uniform improvement and other generalizations over previous methods. We also develop an efficient shortcut for its implementation. We compare the performance of our proposed methods in simulations and apply them to a data set from the UK Biobank.
