Volume 22, Issue 4 (IJIEPM 2012)
2012, 22(4): 319-330 |
Back to browse issues page
Abstract: (19201 Views)
Knowing about the real time of a change in the parameter(s) of a statistical process would enable users to identify root causes more quickly and precisely. Due to the sensitivity and importance of reaching zero defects in high quality processes, to be aware of the change time would be so precious. In this paper, we consider the performance of the Maximum Likelihood Estimator in comparison with built-in change point estimators of Cumulative Sum (CUSUM) and Exponential Weighted Moving Average (EWMA) control charts. Using likelihood function approach, we also proposed a confidence set for the change point. The results of a Monte-Carlo simulation have shown the priority of the MLE in identifying the real time of the change.
Keywords: Statistical Process Control (SPC), Change Point, Simple Step Change, Built-in Estimator, Cumulative Sum control chart (CUSUM), Exponential Weighted Moving Average control chart (EWMA), Average Run Length (ARL), Confidence Set
Type of Study: Research |
Subject:
Other related Industrial and production reserach subjects in which has direct relation to the state-of-the art of the IE
Received: 2012/03/10 | Published: 2012/02/15
Received: 2012/03/10 | Published: 2012/02/15
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |