报告题目： Statistical Inferences for Complex Dependence of Multimodal Imaging Data
报告摘要： Statistical analysis of multimodal imaging data is a challenging task, since the data involves high dimensionality, strong spatial correlations and complex data structures. In this article, we propose rigorous statistical testing procedures for making inferences on the complex dependence of multimodal imaging data. Motivated by the analysis of multi-task fMRI data in the Human Connectome Project (HCP) study, we particularly address three hypothesis testing problems: (a) testing independence among imaging modalities over brain regions, (b) testing independence between brain regions within imaging modalities, and (c) testing independence between brain regions across different modalities. Considering a general form for all the three tests, we develop a global testing procedure and a multiple testing procedure controlling the false discovery rate. We study theoretical properties of the proposed tests and develop a computationally efficient distributed algorithm. The proposed methods and theory are general and relevant for many statistical problems of testing independence structure among the components of high-dimensional random vectors with arbitrary dependence structures. We also illustrate our proposed methods via extensive simulations and analysis of five task fMRI contrast maps in the HCP study.
报告时间：2023年9月5日 (周二)上午10:30-11:30(北京时间)
报告地点：重庆工商大学图书馆第五会议室
报告邀请人：胡雪梅
报告人简介：常晋源，西南财经大学光华特聘教授、博士生导师、数据科学与商业智能联合实验室执行主任、国家杰出青年科学基金获得者、四川省特聘专家、四川省统计专家咨询委员会委员。主要从事“超高维数据分析”和“高频金融数据分析”两个领域的研究。
