论文标题
使用Bootstrap的多元线性模型中的FDP控制
FDP control in multivariate linear models using the bootstrap
论文作者
论文摘要
在本文中，我们开发了一种在多元线性模型中多个感兴趣的对比度，以执行错误发现比例（FDP）的事后推断。为此，我们使用bootstrap从空对比的分布中进行模拟。我们将引导程序与Blanchard（2020）的事后推理结合起来，并证明这样做在所有假设的所有子集上提供了同时对FDP的渐近控制。这要求我们在线性模型中证明多元引导程序的一致性，我们通过Lindeberg Central Limit限制定理进行此操作，提供了比ECK（2018）更简单的结果。通过模拟，我们证明我们的方法可以同时控制所有子集对FDP的控制，并且通常比现有的最新情况更强大。我们说明了从人类连接组项目和慢性阻塞性肺疾病的转录组数据集中的功能磁共振成像数据的方法。
In this article we develop a method for performing post hoc inference of the False Discovery Proportion (FDP) over multiple contrasts of interest in the multivariate linear model. To do so we use the bootstrap to simulate from the distribution of the null contrasts. We combine the bootstrap with the post hoc inference bounds of Blanchard (2020) and prove that doing so provides simultaneous asymptotic control of the FDP over all subsets of hypotheses. This requires us to demonstrate consistency of the multivariate bootstrap in the linear model, which we do via the Lindeberg Central Limit Theorem, providing a simpler proof of this result than that of Eck (2018). We demonstrate, via simulations, that our approach provides simultaneous control of the FDP over all subsets and is typically more powerful than existing, state of the art, parametric methods. We illustrate our approach on functional Magnetic Resonance Imaging data from the Human Connectome project and on a transcriptomic dataset of chronic obstructive pulmonary disease.
