Hoteling�s ??2 control charts are widely used in industries to monitor multivariate processes. The classical estimators, sample\r\nmean, and the sample covariance used in ??2 control charts are highly sensitive to the outliers in the data. In Phase-I monitoring,\r\ncontrol limits are arrived at using historical data after identifying and removing the multivariate outliers. We propose Hoteling�s\r\n??2 control charts with high-breakdown robust estimators based on the reweighted minimum covariance determinant (RMCD)\r\nand the reweighted minimum volume ellipsoid (RMVE) to monitor multivariate observations in Phase-I data. We assessed the\r\nperformance of these robust control charts based on a large number of Monte Carlo simulations by considering different data\r\nscenarios and found that the proposed control charts have better performance compared to existing methods.
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