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  • 统计与管理学院2017年学术报告第76期

    【主 题】 A Diagnostic Procedure for High-Dimensional Data Streams Via Missed Discovery Rate Control

    【报告人】 濮晓龙 教授

    华东师范大学

    【时 间】 2017年12月19日(星期二)10:00-11:00

    【地 点】 上海财经大学统计与管理学院大楼1208会议室

    摘 要】In monitoring complex systems, apart from quick detection of abnormal changes of system performance, accurate fault diagnosis of responsible variables has become critical in many applications that involve high-dimensional data streams. Conventional statistical process control (SPC) diagnostic methods are often computationally expensive. More importantly, as the assumption that only one or a few variables are out-of-control (OC) is invalid for high-dimensional data streams, the fact that they cannot control the missed discovery rate (MDR) will be a major drawback. In this paper, we frame fault isolation as a multiple-testing problem to provide a diagnosis framework by controlling a novel weighted MDR at some level. The use of weights provides an effective strategy to incorporate information on the shift size in large-scale inference. Given the oracle optimality and the data-driven optimality asymptotically, the diagnostic result can be obtained easily and quickly. Simulation results and a real-data analysis from a semiconductor manufacturing process are presented to demonstrate the effectiveness of our method.

    嘉宾简介http://faculty.ecnu.edu.cn/s/1001/main.jspy