報告題目：Large-scale Detection of Differential Sparsity Structure
報 告 人：鄒長亮 教授
所在單位：南開大學
報告時間：2022年11月29日 14:00-15:00
報告地點：騰訊會議：697-194-543
點擊鍊接入會：https://meeting.tencent.com/dm/vYBT2FJm4wsh
校内聯系人：劉天慶 tqliu@jlu.edu.cn
報告摘要：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.
報告人簡介： 鄒長亮，南開大學統計與數據科學學院教授。08年于南開大學獲博士學位，随後留校任教。主要從事統計學及其與數據科學領域的交叉研究和實際應用。研究興趣包括：高維數據統計推斷、大規模數據流分析、變點和異常點檢測等，在Ann.Stat.、Biometrika、 J.Am.Stat.Asso.、Math. Program.、Technometrics、IISE Tran.等統計學和工業工程領域期刊上發表論文幾十篇，主持國家自然科學基金委傑青、優青、重點項目以及重大項目課題等。
