报告时间：7月4日上午10：00-11:00
报告地点：统计楼103
报告人：Nan Chen，National University of Singapore
报告题目： ROBUST MULTIVARIATE CONTROL CHART BASED ON GOODNESS-OF-FIT TEST
Abstract:
This paper proposes a distribution-free multivariate statistical process control (MSPC) chart to detect general distributional changes in multivariate process variables. The chart is deployed based on a multivariate goodness-of-fit test, which is extensible to high dimensional observations. The chart also employs data-dependent control limits, which are computed on line along with the charting statistics, to ensure satisfactory and robust charting performance of the proposed method. Through theoreticaland numerical analyses, we have shown that the proposed chart is exactly distribution-free, and able to operate with unknown in-control distribution or limited reference samples. The chart also has robust IC performance as well as satisfactory OC detection power for general process changes without any assumption of the process distribution. A real-data example in semiconductor production process is presented to demonstrate the application and effectiveness of our method.
Biography:
Nan Chen is an Assistant Professor in the Department of Industrial and Systems Engineering at National University of Singapore. He obtained his B.S. degree in Automation from Tsinghua University, and M.S. degree in Computer Science, M.S. degree in Statistics, and Ph.D. degree in Industrial Engineering all from University of Wisconsin-Madison. His research interests include statistical modeling and surveillance of engineering systems, simulation modeling design, condition monitoring and degradation modeling. He is a member of INFORMS, IIE, and IEEE.