Estimation of the Change Point in Monitoring the Mean of Autocorrelated Processes

Abstract

Knowing the time of the process change could lead to quicker identi-cation of the responsible special cause and less process down time, and itcould help to reduce the probability of incorrectly identifying the specialcause. In this paper, we propose the maximum likelihood estimator (MLE)for the process change point when a control chart is used in monitoring themean of a process in which the observations can be modeled as an AR(1)process plus an additional random error. The performance of the proposedMLE is compared to the performance of the built-in estimator when theyare used in EWMA charts based on the residuals. The results show that theproposed MLE provides good performance in terms of both accuracy andprecision of the estimator.