Adaptive-Sampling-Based High-Dimensional Monitoring with Clustering Information
报 告 人:: 邹长亮
报告地点:: 数学与统计学院四楼学术报告厅
报告时间:: 2017年05月06日星期六17:30-18:00
报告简介:

Monitoring high-dimensional data streams has become increasingly important for real-time detection of abnormal activities in many applications. The challenges associated with designing an efficient change-detection scheme for high-dimensional processes when clustering or spatial information exists are yet to be well addressed; more generally, ignoring the neighboring information of spatially structured data will tend to diminish the detection effectiveness of the traditional detection procedure. We propose a design-adaptive testing procedure to utilize the clustering information when only a limited number of variables can be accessed or observed at each time. Under mild conditions, we derive an optimal sampling strategy 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 the generalized likelihood ratio approach. Benefiting from dynamically estimating the optimal design, the proposed procedure is able to improve the detection sensitivity of existing procedures. Its advantages are demonstrated in numerical simulations and a real data example.

 

举办单位:数学与统计学院
发 布 人:科研助理 发布时间: 2017-05-04
主讲人简介:
邹长亮,现任教于南开大学,教授,国家“万人计划”青年拔尖人才,教育部“青年长江学者”,获国家优秀青年科学基金支持。