在高多重性问题中部署条件随机化测试
Deploying the Conditional Randomization Test in High Multiplicity Problems
摘要：
本文介绍了序贯CRT，这是一个变量选择过程，结合了条件随机化测试（CRT）和选择性SeqStep+。有效的p值通过灵活的CRT构建，然后排序并通过选择性SeqStep+过滤器以生成发现列表。我们开发的理论，保证控制的错误发现率（FDR），即使p值是不独立的。我们在模拟中表明，我们的新程序确实控制了FDR，并且在功率方面与最先进的替代品具有竞争力，有时甚至优于最先进的替代品。最后，我们将我们的方法应用于乳腺癌数据集，目标是识别与癌症阶段相关的生物标志物。
英文摘要：
This paper introduces the sequential CRT, which is a variable selection procedure that combines the conditional randomization test (CRT) and Selective SeqStep+. Valid p-values are constructed via the flexible CRT, which are then ordered and passed through the selective SeqStep+ filter to produce a list of discoveries. We develop theory guaranteeing control on the false discovery rate (FDR) even though the p-values are not independent. We show in simulations that our novel procedure indeed controls the FDR and are competitive with -- and sometimes outperform -- state-of-the-art alternatives in terms of power. Finally, we apply our methodology to a breast cancer dataset with the goal of identifying biomarkers associated with cancer stage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
