时 间：2024年9月26日15:00 - 16:30
地 点：普陀校区理科大楼A1514
报告人：刘耀午 西南财经大学教授
主持人：谌自奇 华东师范大学研究员
摘 要：
Testing a global null is a canonical problem in statistics and has a wide range of applications. In view of the fact that no uniformly most powerful test exists, prior and/or domain knowledge are commonly used to focus on a certain class of alternatives to improve the testing power. However, it is generally challenging to develop tests that are particularly powerful against a certain class of alternatives. In this paper, motivated by the success of ensemble learning methods for prediction or classification, we propose an ensemble framework for testing that mimics the spirit of random forests to deal with the challenges. Our ensemble testing framework aggregates a collection of weak base tests to form a final ensemble test that maintains strong and robust power. We apply the framework to four problems about global testing in different classes of alternatives arising from Whole Genome Sequencing (WGS) association studies. Specific ensemble tests are proposed for each of these problems, and their theoretical optimality is established in terms of Bahadur efficiency. Extensive simulations and an analysis of a real WGS dataset are conducted to demonstrate the type I error control and/or power gain of the proposed ensemble tests.
报告人简介：
刘耀午，西南财经大学统计学院教授。他的研究兴趣包括统计遗传学，大规模假设检验，全基因组关联性分析等。他的多项研究成果发表于JASA，JRSSB, American Journal of Human Genetics等统计学和遗传学知名期刊。