Abstract
Many different control charts have been proposed during the last 30 years for monitoring processes with autocorrelated observations (measurements). The majority of them are developed for monitoring residuals, i.e., differences between the observed and predicted values of the monitored process. Unfortunately, statistical properties of these chart are very sensitive to the accuracy of the estimated model of the underlying process. In this chapter we consider the case when the information from the available data is not sufficient for good estimation of the model. Therefore, we use the concept of model weighted averaging in order to improve model prediction. The novelty of the proposed XWAM control chart consists in the usage of computational intelligence methodology for the construction of alternative models, and the calculation of their weights.
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Hryniewicz, O., Kaczmarek-Majer, K. (2018). Monitoring of Short Series of Dependent Observations Using a XWAM Control Chart. In: Knoth, S., Schmid, W. (eds) Frontiers in Statistical Quality Control 12. Frontiers in Statistical Quality Control. Springer, Cham. https://doi.org/10.1007/978-3-319-75295-2_13
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DOI: https://doi.org/10.1007/978-3-319-75295-2_13
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