10月26日 江苏高校优势学科概率统计前沿系列讲座之九十三

发布时间:2017-10-18   浏览次数:139

报 告 人:濮晓龙 教授(华东师范大学)

报告题目:A Diagnostic Procedure for High-Dimensional Data Streams Via Missed Discovery Rate Control

报告时间:2017年10月26日(周四)下午4:30

报告地点:静远楼1506报告厅

报告人简介:

    濮晓龙,华东师范大学统计学教授,博士生导师。研究领域为应用统计,方向为序贯分析与统计质量管理,擅长利用统计方法解决各类实际问题。发表论文30 多篇、出版专著1 本、参编教材8本、参编译著3本、主持制定国家标准一项。现主持国家自然科学基金项目、上海市科研项目各一项以及横向项目多项。先后完成了来自国家基金委、军工系统以及多个实际部门的多项课题。先后获得军队科技进步一等奖( 2006) 、军队科技进步二等奖( 2011) 以及上海市优秀成果一等奖(2007) 各一项。中国现场统计研究会副理事长、全国工业统计学教学研究会副会长、中国质量协会常务理事、全国统计方法应用标准化技术委员会委员、中国质量教育委员会委员、中国六西格玛推进工作委员会委员、上海市质量技术应用统计学会副理事长等,《 数理统计与管理》杂志副主编。

报告摘要:

    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.