A Bayesian EWMA method to detect jumps at the start‐up phase of a process

Abstract

AbstractThe start‐up phase data of a process are the spine of traditional SPC charting and testing methods and are usually assumed to be i.i.d. observations from the in‐control distribution. In this work a new method is proposed to model normally distributed start‐up phase data where we allow for serial dependence and randomly occurring unidirectional level shifts of the underlying parameter of interest. The theoretic development is based on a Bayesian sequentially updated EWMA model with normal mixture errors. The new approach makes use of available prior information and provides a framework for drawing decisions and making prediction on line, even with a single observation. Copyright © 2008 John Wiley & Sons, Ltd.