学术报告

Large-scale Detection of Differential Sparsity Structure-邹长亮教授(南开大学)

报告题目:Large-scale Detection of Differential Sparsity Structure

报告人:邹长亮 教授(南开大学)

摘要:Two-sample multiple testing has a wide range of applications. Most of the literature considers simultaneous tests of equality of parameters. This work takes a different perspective and investigates the null hypotheses that the two support sets are equal. This formulation of the testing problem is motivated by the fact that in many applications where the two parameter vectors being compared are both sparse, we might be more concerned about the detection of differential sparsity structures rather than the difference in parameter magnitudes. A general approach to problems of this type is developed via a novel double thresholding (DT) filter. The DT filter first constructs a sequence of pairs of ranking statistics that fulfill global symmetry properties, and then chooses two data-driven thresholds along the ranking to simultaneously control the false discovery rate (FDR) and maximize the number of rejections. Several applications of the methodology are given, including tests for large-scale correlation matrices, high-dimensional linear models and Gaussian graphical models.

报告人简介:邹长亮  南开大学统计与数据科学学院教授。08年于南开大学获博士学位,随后留校任教。主要从事统计学及其与数据科学领域的交叉研究和实际应用。研究兴趣包括:高维数据统计推断、大规模数据流分析、变点和异常点检测等,在Ann.Stat.、Biometrika、 J.Am.Stat.Asso.、Math. Program.、Technometrics、IISE Tran.等统计学和工业工程领域期刊上发表论文几十篇,主持国家自然科学基金委杰青、优青、重点项目以及重大项目课题等。

报告时间:2022年12月2日(周五)下午15:00

线上参加:#腾讯会议 523-3915-6716

联系人:周洁

举办单位:数学科学学院、交叉科学研究院、北京国家应用数学中心


2022年12月2日-邹长亮教授(南开大学)-周洁.jpg