报告题目：Fault classification for high-dimensional data streams: A directional diagnostic framework via multiple testing
报 告 人：李文东 博士 上海财经大学
报告时间：2021年12月1日 9:00-10:00
报告地点：腾讯会议ID：810243114 密码：1201
校内联系人：李聪 li_cong@jlu.edu.cn
报告摘要：In various modern statistical process control applications that involve high-dimensional data streams (HDDS), accurate fault diagnosis of out-of-control (OC) streams is becoming crucial. The existing diagnostic approaches either focus on moderate-dimensional processes or are unable to determine the shift direction accurately, especially when the signal-to-noise ratio is low. In this paper, we consider the fault classification problem of the mean vector of HDDS where determining the shift direction of the OC streams is important to perform customized repairs. To this end, under the basic assumption that the high-dimensional observations after the alarm are solely OC, the problem is formulated into a three-classification multiple testing framework, and an efficient data-driven diagnostic procedure is developed to minimize the expected number of false positives and to control the missed discovery rate at given level. The procedure is statistically optimal and computationally efficient, and improves the diagnostic effectiveness by considering directional information, which provides insights to guide further decisions. Both theoretical and numerical results reveal the superiority of the new method.
报告人简介：李文东，上海财经大学统计与管理学院讲师，硕士生导师。2019年于华东师范大学获得统计学博士学位，期间曾访问香港科技大学工业工程与决策分析系与美国佛罗里达老员工物统计系。现为东北师范大学数学与统计学院访问学者。研究兴趣为质量控制、多重检验、因果推断，成果多发表于Technometrics、IISE Transactions、Journal of Quality Technology、Naval Research Logistics等，入选上海市超级博士后激励计划。
