Abstract
Control charts are quality control procedures to detect a change of manufacturing product. They are used both univariate and multivariate quality characteristics which are random, independent and identically distributed (i.i.d.). Copulas approach can be applied to dependencies and non-normal variables with marginal distributions when the joint distribution could not be found in multivariate cases except normal distribution process. Therefore, the objective of this work is to compare three control charts from three copulas, when observations are exponential distribution via Average Run Length ( ARL ). For specifying dependence between random variables are proposed and measured by Kendall’s tau through Monte Carlo simulation . The numerical results show that the Normal copula of Multivariate Exponentially Weighted Moving Average (MEWMA ) control chart is inferior to other control charts for all magnitudes of shifts.
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Acknowledgments
The author would like to express my gratitude to Department of Applied Statistics, Faculty of Sciences, King Mongkut’s University of Technology, North Bangkok, Thailand for supporting the supercomputer.
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Kuvattana, S., Sukparungsee, S. (2017). Comparative the Performance of Control Charts Based on Copulas. In: Ao, SI., Kim, H., Amouzegar, M. (eds) Transactions on Engineering Technologies. WCECS 2015. Springer, Singapore. https://doi.org/10.1007/978-981-10-2717-8_4
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