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Open AccessArticle

A Nonparametric HEWMA-p Control Chart for Variance in Monitoring Processes

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Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia
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Department of Statistics, School of Mathematical Sciences, CNMS, The University of Dodoma, Dodoma P.O. Box 259, Tanzania
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Department of Industrial and Management Engineering, POSTECH, Pohang 790-784, Korea
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(3), 356; https://doi.org/10.3390/sym11030356
Received: 12 February 2019 / Revised: 28 February 2019 / Accepted: 6 March 2019 / Published: 9 March 2019
Control charts are considered as powerful tools in detecting any shift in a process. Usually, the Shewhart control chart is used when data follows the symmetrical property of a normal distribution. In practice, the data from the industry may follow a non-symmetrical distribution or an unknown distribution. The average run length (ARL) is a significant measure to assess the performance of the control chart. The ARL may mislead when the statistic is computed from an asymmetric distribution. To handle this issue, in this paper, an ARL-unbiased hybrid exponentially weighted moving average proportion (HEWMA-p) chart is proposed for monitoring the process variance for a non-normal distribution or an unknown distribution. The efficiency of the proposed chart is compared with the existing chart in terms of ARLs. The proposed chart is more efficient than the existing chart in terms of ARLs. A real example is given for the illustration of the proposed chart in the industry. View Full-Text
Keywords: Binomial distribution; hybrid exponentially weighted moving average statistic; unknown distribution; variance; average run length Binomial distribution; hybrid exponentially weighted moving average statistic; unknown distribution; variance; average run length
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Aslam, M.; Rao, G.S.; AL-Marshadi, A.H.; Jun, C.-H. A Nonparametric HEWMA-p Control Chart for Variance in Monitoring Processes. Symmetry 2019, 11, 356.

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