学术报告
报告题目：A Robust Multivariate EWMA Control Chart for Detecting Sparse Mean Shifts
报 告 人：濮晓龙 教授 华东师范大学 统计学院院长
时 间：2015年10月14日(星期三)上午10:30
地 点：数学楼第七教室
摘 要:In multivariate statistical process control (MSPC) applications, process mean shifts often occur in only a few of components. To solve this MSPC problem, many control charts were proposed in the literature. Most of these charts assumed that the multivariate quality characteristics are normally distributed. Among them, the control chart proposed by Zou and Qiu (2009), integrating the least absolute shrinkage and selection operator (LASSO) method into the EWMA scheme, has the best overall performance. In this paper, we extend the classical multivariate LASSO control chart to a robust version that is affine-invariant and has a strictly distribution-free property over a broad class of population models, indicating that the in-control run length distribution can attain or is always very close to the nominal one when using the same control limit designed for a multivariate normal distribution. Our simulation results show that the proposed method is very efficient in detecting various sparse shifts for heavy-tailed and skewed multivariate distributions. In addition, it is easy to implement with an iterative algorithm and the least angle regression (LARS) algorithm. A white wine data illustrates that the proposed control chart performs quite well in applications.
欢迎广大师生参加！
统计研究院
2015年10月9日