Performance evaluation of conventional exponentially weighted moving average (EWMA) and p-value cumulative sum (CUSUM) control chart

Using exponentially weighted moving average (EWMA) charts

1. Unlike X -R and Individuals charts (without the Western Electric Handbook rules which aim to increase sensitivity), all of the data collected over time may be used to determine the control status of a process. 2. The EWMA is often superior to the CUSUM charting technique for detecting "larger" shifts. 3. EWMA schemes may be applied for monitoring standard deviations in addition to the proces...

Risk-adjusted survival time monitoring with an updating exponentially weighted moving average (EWMA) control chart.

Monitoring medical outcomes is desirable to help quickly detect performance changes. Previous applications have focused mostly on binary outcomes, such as 30-day mortality after surgery. However, in many applications the survival time data are routinely collected. In this paper, we propose an updating exponentially weighted moving average (EWMA) control chart to monitor risk-adjusted survival t...

Evaluation of Cummulative Sum (cusum) and Exponentially Weighted Moving Average (ewma) Control Charts to Detect Changes in Underlying Demand Trends of Naval Aviation Spares

Average Run Length Performance for Multivariate Exponentially Weighted Moving Average Control Chart Procedure with Application

The drawbacks to multivariate charting schemes is their inability to identify which variable was the source of the signal. The multivariate exponentially weighted moving average (MEWMA) developed by Lowry, et al (1992) is an example of a multivariate charting scheme whose monitoring statistic is unable to determine which variable caused the signal. In this paper, the run length performance of m...

Mixed Cumulative Sum-Exponentially Weighted Moving Average Control Charts: An Efficient Way of Monitoring Process Location

Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) charts are famous statistical tools, to handle special causes and to bring the process back in statistical control. Shewhart charts are useful to detect large shifts, whereas EWMA and CUSUM are more sensitive for small to moderate shifts. In this study, we propose a new control chart, named mixed CUSUM-EWMA chart...