报告题目: Large-Scale Datastreams Surveillance via Pattern-Oriented-Sampling

报 告 人: 邹长亮 教授/副院长 南开大学统计与数据科学学院

照    片：

邀 请 人: 薄立军，冶继民

报告时间: 2020年9月10日(周四)10:00开始

报告地点：腾讯会议ID：230 606 623

报告人简介：邹长亮 南开大学统计与数据科学学院教授，副院长。2008年毕业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权威期刊上发表论文几十篇，主持国家自然科学基金委重大项目课题、优青项目、杰青项目等。

报告摘要: Monitoring large-scale datastreams with limited resources has become increasingly important for real-time detection of abnormal activities in many applications. Despite the availability of large datasets, the challenges associated with designing an efficient change-detection when clustering or spatial pattern exists are not yet well addressed. In this talk, I will introduce a design-adaptive testing procedure when only a limited number of streaming observations can be accessed at each time. We derive an optimal sampling strategy, the pattern-oriented-sampling, with which the proposed test possesses asymptotically and locally best power under alternatives. Then, a sequential change-detection procedure is proposed by integrating this test with generalized likelihood ratio approach. Benefiting from dynamically estimating the optimal sampling design, the proposed procedure can improve the sensitivity in detecting clustered changes compared with existing procedures. Its advantages are demonstrated in numerical simulations and a real data example. Ignoring the neighboring information of spatially structured data will tend to diminish the detection effectiveness of traditional detection procedures.