讲座题目: Large-Scale Covariate Assisted Two-Sample Inference under Dependence

主 讲 人：东北师范大学朱文圣教授

讲座时间：2020年12月25日（周五）19:30开始

腾讯会议号：588664891

主办单位：yh86银河国际  浙江省2011“数据科学与大数据分析协同创新中心”

摘  要：

The problems of large-scale two-sample inference often arise from the statistical analysis of “high throughput” data. The conventional multiple testing procedures for large-scale two-sample inference usually suffer from substantial loss of testing efficiency when conducting numerous two-sample t-tests directly. To some extent, this is due to the ignorance of sparsity information in large-scale two-sample inference. Moreover, in practice, the two-sample tests commonly have local correlations and neglecting the dependence structure in the two-sample tests may decrease the statistical accuracy in multiple testing. Therefore, it is imperative to develop a multiple testing procedure which not only takes into account the sparsity information but also accommodates the dependence structure among the tests. To address the aforementioned important issues, we start by introducing a novel dependence model to allow for sparsity information and to characterize the dependence structure among the tests. Based on the dependence model, we propose a Covariate Assisted Local Index of Significance (COALIS) procedure and show that it is valid and optimal in some sense. Then a data-driven procedure is developed to mimic the oracle procedure and simulations show that the COALIS procedure outperforms its competitors. Finally, we apply the COALIS procedure to the dosage response data.

主讲人简介：

朱文圣，东北师范大学数学与统计学院教授、博士生导师、副院长。2006年博士毕业于东北师范大学，2008-2010年在耶鲁大学做博士后研究，2015-2017年访问北卡罗来纳大学教堂山分校。中国现场统计研究会计算统计分会副理事长、数据科学与人工智能分会秘书长，中国概率统计学会副秘书长，吉林省现场统计研究会秘书长。研究方向为生物统计与精准医疗，在JASA、Test、NeuroImage、中国科学等杂志发表学术论文多篇，主持并完成国家自然科学基金项目，入选吉林省第七批拔尖创新人才。

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