﻿ 数学学院、所2019年系列学术活动（第167场）：朱文圣教授 东北师范大学-数学学院

### 数学学院、所2019年系列学术活动（第167场）：朱文圣教授 东北师范大学

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 can not only take into account the sparsity information but also accommodate the dependence structure among the tests. To address the aforementioned important issues, we first introduce 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 COALIS procedures outperform their competitors. Finally, we apply COALIS procedure to the dosage response data.

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。200612月博士毕业于东北师范大学，201312月起任东北师范大学数学与统计学院教授。2008-2010年在耶鲁大学做博士后研究，2015-2017年访问北卡大学教堂山分校。现兼任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等。主要从事统计学的方法与应用研究，在统计学国际顶级期刊Journal of the American Statistical Association (JASA)、医学图像著名期刊NeuroImage等发表学术论文多篇。主持并完成国家自然科学基金项目多项。

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