统计学系列 Seminar 第86期
主 题：Design Variable-Sampling Control Charts Using Covariate Information
会议时间：2021 年 6 月 29 日（周二）上午9:00-11:00
会议工具：线上讲座（腾讯会议，会议号：992 728 370）
Statistical process control charts are widely used in the manufacturing industry for monitoring the performance of sequential production processes over time. A common practice in using a control chart is to first collect samples and take measurements of certain quality variables from them at equally-spaced sampling times, and then make decisions about the process status by the chart based on the observed data. In some applications, however, the quality variables are associated with certain covariates, and it should improve the performance of a control chart if the covariate information can be used properly. Intuitively, if the covariate information indicates that the process under monitoring is likely to have a distributional shift soon based on the established relationship between the quality variables and the covariates, then it should benefit the process monitoring by collecting the next process observation sooner than usual. Motivated by this idea, we propose a general framework to design a variable sampling control chart by using covariate information. Our proposed chart is self-starting and can well accommodate stationary short-range serial data correlation. It is the first variable sampling control chart in the literature that the sampling intervals are determined by the covariate information. Numerical studies show that the proposed method performs well in different cases considered.
杨凯，南京大学数学系学士，美国佛罗里达大学生物统计专业五年级博士生，即将就任威斯康星医学院助理教授。主要研究方向包括时空数据的建模与监控、统计过程控制中协变量信息的使用。以第一作者或通讯作者在 Technometrics， IISE Transactions 和 Statistics in Medicine 等统计、工业工程领域重要期刊上发表过多篇学术论文，并获得过美国统计协会 Young Investigator Award 和国际印度统计协会 Best Paper Award。