Paired Test of Matrix Graphs
报 告 人:: 夏寅
报告地点:: 数学与统计学院415报告厅
报告时间:: 2018年11月08日星期四16:30-17:30

Quantifying significance of interactions of brain regions and inferring brain connectivity network is of paramount importance in neuroscience. Although there have recently emerged some tests for graph inference based on the neuroimaging data in the form of a spatial temporal matrix, there is no readily available solution to test the change of brain network before and after a stimulus activity. In this talk, we develop a paired test of matrix graphs to infer brain connectivity network when the groups of samples are correlated. The proposed test statistic is both bias corrected and variance corrected, which theoretically guarantees the subsequent multiple testing procedure built on this test statistic can asymptotically control the false discovery rate at the pre-specified level. Both the methodology and theory of the new method are considerably different from the two independent samples framework, due to the strong correlations of measurements on the same subjects before and after the stimulus. We illustrate the efficacy of our proposal through both simulations, and an analysis of an Alzheimer's Disease Neuroimaing Initiative dataset.


发 布 人:吴双 发布时间: 2018-11-07
夏寅(复旦大学研究员、博导),2013年毕业于宾夕法尼亚大学沃顿商学院,2013-2016年在美国北卡大学教堂山分校任tenure track Assistant Prof。2016年入选中组部千人计划青年项目。研究方向包括高维统计推断、大范围检验及应用等。在JASA, AOS, JRSSB, Biometrika等期刊上发表多篇论文。