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.