时 间：2022年11月29日（周二） 10:00-11:00
地 点：腾讯会议 ：992-165-109 会议密码：1309 线下地点：理科大楼A1514
题 目：Large-scale Detection of Differential Sparsity Structure with FDR Control
报告人：任好洁 副教授 上海交通大学
主持人：刘玉坤 教授
主 办：统计学院
摘 要：
Two-sample multiple testing has a wide range of applications. Most of the literature considers simultaneous tests of equality of parameters. This talk 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, one might be more concerned about the detection of differential sparsity structures rather than the difference in parameter magnitudes. Focusing on this type of problems, we develop a general approach, which adapts the newly proposed symmetry data aggregation tool combined with 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 high-dimensional linear models and Gaussian graphical models. We show that the proposed method is able to asymptotically control the FDR under certain conditions. Numerical results confirm the effectiveness and robustness of DT in FDR control and detection ability in many settings.
报告人简介：
任好洁，上海交通大学数学科学学院长聘教轨副教授，2018年博士毕业于南开大学，随后在宾州州立大学从事博士后研究，导师是李润泽教授。研究方向包括统计异常探查、在线学习与监控、高维数据推断等。在JASA，Biometrika等杂志上发表论文10余篇。