Simultaneous false discovery proportion bounds via knockoffs and closed testing
提出了基于knockoffs的新方法，通过封闭检验和多重加权和局部检验统计量，获得更优的错误发现比例界限，并在模拟和英国生物银行数据中验证了性能。
Abstract 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.