Work

A Markov Chain Model for Approximating the Run Length Distributions of Poisson EWMA Charts under Linear Drifts

Year: 2022

Type: article

Abstract: In addition to monitoring the Poisson mean rate with step shifts, increasing attention has been given to monitoring Poisson processes subject to linear trends. The exponentially weighted moving average (EWMA) control chart has been widely implemented to monitor normal processes, but it lacks investigation for detecting the Poisson mean change under a linear trend. In this paper, we analyze the performance of the EWMA chart by extending the Markov chain model from monitoring Poisson processes under a step shift to a Poisson process with linear drift. The results demonstrate that the proposed method is able to provide accurate average run length approximation, compared with the Monte Carlo simulation. Optimal design tables and sensitivity analysis are presented to facilitate the use of the EWMA chart in practice.

Source: Mathematics

DOI: 10.3390/math10244786

Authors: Honghao Zhao, Huajun Tang, Chuan Pang, Huimin Jiang

Institution: Macau University of Science and Technology

Language: English

Topic: Advanced Statistical Process Monitoring

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,885

Funder: National Natural Science Foundation of China