论文标题
通过有条件校准改善仿冒品
Improving knockoffs with conditional calibration
论文作者
论文摘要
基于引入综合预测变量，以控制错误发现率（FDR），理发和糖果的仿制过滤器（ARXIV：1404.5609）是用于多次测试的灵活框架。使用Fithian和Lei的条件校准框架（ARXIV：2007.10438），我们介绍了校准的仿制程序，该方法均匀地提高了任何固定X或Model-X仿型程序的功能。我们从理论和经验上表明，在两种情况下，仿冒方法几乎无能为力的两种情况下，改进尤其值得注意：当拒绝集很小时，以及固定X仿基中的设计矩阵的结构时，可以阻止我们构建良好的仿制变量。在这些情况下，校准的仿冒品甚至超过了竞争性的FDR控制方法，例如（依赖调整后的）程序Benjamini-Hochberg在许多情况下。
The knockoff filter of Barber and Candes (arXiv:1404.5609) is a flexible framework for multiple testing in supervised learning models, based on introducing synthetic predictor variables to control the false discovery rate (FDR). Using the conditional calibration framework of Fithian and Lei (arXiv:2007.10438), we introduce the calibrated knockoff procedure, a method that uniformly improves the power of any fixed-X or model-X knockoff procedure. We show theoretically and empirically that the improvement is especially notable in two contexts where knockoff methods can be nearly powerless: when the rejection set is small, and when the structure of the design matrix in fixed-X knockoffs prevents us from constructing good knockoff variables. In these contexts, calibrated knockoffs even outperform competing FDR-controlling methods like the (dependence-adjusted) procedure Benjamini-Hochberg in many scenarios.
