A fundamental hypothesis in theoretical statistical quality control is that samples are independently and identically distributed; but this assumption is frequently violated in many production processes. Moreover, the presence of autocorrelated data in many process control applications gravely affects the performance of classical control charts if not appropriately accounted for. In this paper, bootstrap T2 and bootstrap multivariate exponential weighted moving average (BMEWMA) control charts are proposed for monitoring and controlling multivariate autocorrelated processes. From numerical illustration, results obtained from the Average Run Length (ARL), standard deviation run length (SDRL), median run length (MRL) and percentiles run length (PRL) displayed in tabular and graphical forms, shows that the proposed bootstrap control methods performed better than the F-distribution T2 control method.
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