论文标题
仿制的力量：排名算法，增强设计和对称统计的影响
Power of Knockoff: The Impact of Ranking Algorithm, Augmented Design, and Symmetric Statistic
论文作者
论文摘要
仿基滤波器是针对高维线性模型的最新错误发现率（FDR）控制方法。我们指出，仿基有三个关键组成部分：排名算法，增强设计和对称统计量，每个组件都可以接受多种选择。通过考虑三个组件的各种组合，我们获得了仿冒品变体的集合。所有这些变体保证了有限样本的FDR控制，我们的目标是比较其力量。我们在回归系数上假设一个罕见且弱的信号模型，并通过推导假阳性率和假阴性速率的显式公式来比较不同仿冒品变体的功率。我们的结果提供了有关如何在目标水平控制FDR时如何提高功率的新见解。我们还将仿制的力量与其propotype进行了比较 - 一种使用相同排名算法但可以访问理想阈值的方法。该比较揭示了一个人通过找到数据驱动的阈值来控制FDR来支付的额外价格。
The knockoff filter is a recent false discovery rate (FDR) control method for high-dimensional linear models. We point out that knockoff has three key components: ranking algorithm, augmented design, and symmetric statistic, and each component admits multiple choices. By considering various combinations of the three components, we obtain a collection of variants of knockoff. All these variants guarantee finite-sample FDR control, and our goal is to compare their power. We assume a Rare and Weak signal model on regression coefficients and compare the power of different variants of knockoff by deriving explicit formulas of false positive rate and false negative rate. Our results provide new insights on how to improve power when controlling FDR at a targeted level. We also compare the power of knockoff with its propotype - a method that uses the same ranking algorithm but has access to an ideal threshold. The comparison reveals the additional price one pays by finding a data-driven threshold to control FDR.
