Abstract
The MaxEWMA chart has recently been introduced as an improvement over the standard EWMA chart for detecting changes in the mean and/or standard deviation of a normally distributed process. Although this chart was originally developed for normally distributed process data, its robustness to violations of the normality assumption is the central theme of this study. For data distributions with heavy tails or displaying strong skewness, the in-control average run lengths (ARLs) for the MaxEWMA chart are shown to be significantly shorter than expected. On the other hand, out-of-control ARLs are comparable to normal theory values for a variety of symmetric non-normal distributions. The MaxEWMA chart is not robust to skewness.
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