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

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dc.creator Aslam, Muhammad
dc.creator Rao, Gadde Srinivasa
dc.creator AL-Marshadi, Ali Hussein
dc.creator Jun, Chi-Hyuck
dc.date 2020-03-13T07:57:56Z
dc.date 2020-03-13T07:57:56Z
dc.date 2019
dc.date.accessioned 2022-10-20T13:09:14Z
dc.date.available 2022-10-20T13:09:14Z
dc.identifier 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.
dc.identifier http://hdl.handle.net/20.500.12661/2157
dc.identifier.uri http://hdl.handle.net/20.500.12661/2157
dc.description Full Text Article. Also available at: doi:10.3390/sym11030356
dc.description 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.
dc.language en
dc.publisher Symmetry
dc.subject Binomial distribution
dc.subject Unknown distribution
dc.subject Variance
dc.subject Average run length
dc.subject ARL
dc.subject HEWMA-p
dc.subject Hybrid exponentially weighted moving average proportion
dc.subject Hybrid exponentially weighted moving average statistic
dc.title A nonparametric HEWMA-p control chart for variance in monitoring processes
dc.type Article


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