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The presence of several outliers in an individuals retrospective multivariate control chart distorts both the sample mean vector and covariance matrix, making the classical Hotelling's T2 approach unreliable for outlier detection. To overcome the distortion or masking, we propose a computer-intensive multistep cluster-based method. Compared with classical and robust estimation procedures, simulation studies show that our method is usually better and sometimes much better at detecting randomly occurring outliers as well as outliers arising from shifts in the process location. Additional comparisons based on real data are given.
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Jobe et al. (2009) studied this question.
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