A nonparametric HEWMA-p control chart for variance in monitoring processes

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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
Symmetry
Abstract
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.
Description
Full Text Article. Also available at: doi:10.3390/sym11030356
Keywords
Binomial distribution, Unknown distribution, Variance, Average run length, ARL, HEWMA-p, Hybrid exponentially weighted moving average proportion, Hybrid exponentially weighted moving average statistic
Citation
Aslam, M., Rao, G. S., Al-Marshadi, A. H., & Jun, C. H. (2019). A nonparametric HEWMA-p control chart for variance in monitoring processes. Symmetry, 11(3), 356.
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