时 间：2022年10月24日10:30-12:00
地 点：ZOOM会议ID: 8155-9570-657 密码: 686474
报告人：史成春 助理教授
主持人：周勇 教授
摘 要：
A central question in high-dimensional mediation analysis is to infer the significance of individual mediators. The main challenge is that the total number of potential paths that go through any mediator is super-exponential in the number of mediators. Most existing mediation inference solutions either explicitly impose that the mediators are conditionally independent given the exposure, or ignore any potential directed paths among the mediators. In this article, we propose a novel hypothesis testing procedure to evaluate individual mediation effects, while taking into account potential interactions among the mediators. Our proposal thus fills a crucial gap, and greatly extends the scope of existing mediation tests. Our key idea is to construct the test statistic using the logic of Boolean matrices, which enables us to establish the proper limiting distribution under the hypothesis. We further employ screening, data splitting, and decorrelated estimation to reduce the bias and increase the power of the test. We show that our test can control both the size and false discovery rate asymptotically, and the power of the test approaches one, while allowing the number of mediators to diverge to infinity with the sample size. We demonstrate the efficacy of the method through simulations and a neuroimaging study of Alzheimer’s disease.
报告人简介：
史成春是伦敦政治经济学院统计系的助理教授。他有近20篇第一作者同行评审的文章被顶级统计期刊AOS，JRSSB，JASA和JMLR接受。他还在顶级机器学习会议ICML 和 NeurIPS上发表了论文。他目前担任 JRSSB 和 Journal of Nonparametric Statistics 的副主编。他的研究主要是在强化学习中开发统计方法。他是 2021年皇家统计学会研究奖的获得者。他还获得三次 IMS Travel Award。