论文标题
多个仿制的聚集
Aggregation of Multiple Knockoffs
论文作者
论文摘要
我们开发了Barber and Candes（2015）引入的仿冒推理程序的扩展。这种称为多个仿冒品（AKO）聚合的新方法解决了基于仿基推断的随机性质固有的不稳定性。具体而言，与原始仿制算法相比，AKO可以提高稳定性和功率，同时仍保持虚假发现率控制的保证。我们提供了一个新的推理过程，证明其核心属性，并在一组合成和真实数据集的实验中证明了其优势。
We develop an extension of the Knockoff Inference procedure, introduced by Barber and Candes (2015). This new method, called Aggregation of Multiple Knockoffs (AKO), addresses the instability inherent to the random nature of Knockoff-based inference. Specifically, AKO improves both the stability and power compared with the original Knockoff algorithm while still maintaining guarantees for False Discovery Rate control. We provide a new inference procedure, prove its core properties, and demonstrate its benefits in a set of experiments on synthetic and real datasets.
