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

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dc.creatorAslam, Muhammad
dc.creatorRao, Gadde Srinivasa
dc.creatorAL-Marshadi, Ali Hussein
dc.creatorJun, Chi-Hyuck
dc.date2020-03-13T07:57:56Z
dc.date2020-03-13T07:57:56Z
dc.date2019
dc.date.accessioned2022-10-20T13:09:14Z
dc.date.available2022-10-20T13:09:14Z
dc.descriptionFull Text Article. Also available at: doi:10.3390/sym11030356
dc.descriptionControl 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.identifierAslam, 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.identifierhttp://hdl.handle.net/20.500.12661/2157
dc.identifier.urihttp://hdl.handle.net/20.500.12661/2157
dc.languageen
dc.publisherSymmetry
dc.subjectBinomial distribution
dc.subjectUnknown distribution
dc.subjectVariance
dc.subjectAverage run length
dc.subjectARL
dc.subjectHEWMA-p
dc.subjectHybrid exponentially weighted moving average proportion
dc.subjectHybrid exponentially weighted moving average statistic
dc.titleA nonparametric HEWMA-p control chart for variance in monitoring processes
dc.typeArticle

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