Monitoring multivariate coefficient of variation with upward Shewhart and EWMA charts in the presence of measurement errors using the linear covariate error model |
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Authors: | Heba N. Ayyoub Michael B. C. Khoo Ming Ha Lee Abdul Haq |
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Affiliation: | 1. School of Mathematical Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia;2. Faculty of Engineering, Computing and Science, Swinburne University of Technology, Kuching, Sarawak, Malaysia;3. Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan |
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Abstract: | In practice, measurement errors exist and ignoring their presence may lead to erroneous conclusions in the actual performance of control charts. The implementation of the existing multivariate coefficient of variation (MCV) charts ignores the presence of measurement errors. To address this concern, the performances of the upward Shewhart-MCV and exponentially weighted moving average MCV charts for detecting increasing MCV shifts, using a linear covariate error model, are investigated. Explicit mathematical expressions are derived to compute the limits and average run lengths of the charts in the presence of measurement errors. Finally, an illustrative example using a real-life dataset is presented to demonstrate the charts’ implementation. |
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Keywords: | EWMA chart linear covariate error model Markov chain measurement errors multivariate coefficient of variation Shewhart chart |
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