论文标题
降低的仿冒品：利用电子价值进行虚假发现率控制
Derandomized knockoffs: leveraging e-values for false discovery rate control
论文作者
论文摘要
Model-X仿基是用于高维回归算法的灵活包装器方法，可保证控制错误发现率（FDR）。由于该方法固有的随机性，同一数据集上的Model-X衰减的不同运行通常会导致不同的选定变量集，这在实践中是不希望的。在本文中，我们介绍了一种具有可证明的FDR控制的模型-X仿制的方法。我们提出的方法的关键见解在于发现仿那程序本质上是E-BH程序。我们利用这种连接，并通过汇总由多个仿冒实现产生的电子价值来降低模型-x仿冒品。我们证明，降低的过程在所需的水平上控制FDR，而没有任何其他条件（相反，先前提出的降低方法无法保证FDR控制）。通过数值实验评估了所提出的方法，在其中，与模型-X仿冒品相比，降低的过程可实现可比的功率，并且选择变异性大大降低。
Model-X knockoffs is a flexible wrapper method for high-dimensional regression algorithms, which provides guaranteed control of the false discovery rate (FDR). Due to the randomness inherent to the method, different runs of model-X knockoffs on the same dataset often result in different sets of selected variables, which is undesirable in practice. In this paper, we introduce a methodology for derandomizing model-X knockoffs with provable FDR control. The key insight of our proposed method lies in the discovery that the knockoffs procedure is in essence an e-BH procedure. We make use of this connection, and derandomize model-X knockoffs by aggregating the e-values resulting from multiple knockoff realizations. We prove that the derandomized procedure controls the FDR at the desired level, without any additional conditions (in contrast, previously proposed methods for derandomization are not able to guarantee FDR control). The proposed method is evaluated with numerical experiments, where we find that the derandomized procedure achieves comparable power and dramatically decreased selection variability when compared with model-X knockoffs.
