Engmann Gideon Mensah, Han Dong
School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Department of Statistics, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana.
J Appl Stat. 2021 Jan 12;49(6):1540-1558. doi: 10.1080/02664763.2020.1870670. eCollection 2022.
This article considers the problem of jointly monitoring the mean and variance of a process by multi-chart schemes. Multi-chart is a combination of several single charts which detects changes in a process quickly. Asymptotic analyses and simulation studies show that the optimized CUSUM multi-chart has optimal performance than optimized EWMA multi-chart in jointly detecting mean and variance shifts in an normal observation. A real example that monitors the changes in IBM's stock returns (mean) and risks (variance) is used to demonstrate the performance of the above two multi-charts. The proposed method has been compared to a benchmark and it performed better.
本文考虑了通过多图方案联合监测过程均值和方差的问题。多图是几个单图的组合，能够快速检测过程中的变化。渐近分析和模拟研究表明，在正态观测中联合检测均值和方差变化时，优化后的累积和（CUSUM）多图比优化后的指数加权移动平均（EWMA）多图具有更优的性能。通过一个监测IBM股票收益（均值）和风险（方差）变化的实际例子来展示上述两种多图的性能。所提出的方法与一个基准进行了比较，且表现更优。
