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New extended distribution-free homogenously weighted monitoring schemes for monitoring abrupt shifts in the location parameter

dc.contributor.authorLetshedi, Tokelo Irene
dc.contributor.authorMalela-Majika, Jean-Claude
dc.contributor.authorShongwe, Sandile Charles
dc.date.accessioned2022-11-07T10:16:09Z
dc.date.available2022-11-07T10:16:09Z
dc.date.issued2022-01-21
dc.descriptionDATA AVAILABILITY STATEMENT : The data used for the illustration example are available from Mukherjee et al. (2019) (10.1016/j.cie.2019.106059).en_US
dc.descriptionSUPPLEMENTARY MATERIAL : S1 Appendix. Properties of the HWMA W scheme. https://doi.org/10.1371/journal.pone.0261217.s001en_US
dc.descriptionS2 Appendix. Properties of the DHWMA W scheme. https://doi.org/10.1371/journal.pone.0261217.s002en_US
dc.descriptionS3 Appendix. Properties of the HHWMA W chart.en_US
dc.description.abstractA homogeneously weighted moving average (HWMA) monitoring scheme is a recently proposed memory-type scheme that gained its popularity because of its simplicity and superiority over the exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) schemes in detecting small disturbances in the process. Most of the existing HWMA schemes are designed based on the assumption of normality. It is well-known that the performance of such monitoring schemes degrades significantly when this assumption is violated. Therefore, in this paper, three distribution-free monitoring schemes are developed based on the Wilcoxon rank-sum W statistic. First, the HWMA W scheme is introduced. Secondly, the double HWMA (DHWMA) W scheme is proposed to improve the ability of the HWMA W scheme in detecting very small disturbances in the location parameter and at last, the hybrid HWMA (HHWMA) W scheme is also proposed because of its flexibility and better performance in detecting shifts of different sizes. The zero-state performances of the proposed schemes are investigated using the characteristics of the run-length distribution. The proposed schemes outperform their existing competitors, i.e. EWMA, CUSUM and DEWMA W schemes, in many situations, and particularly the HHWMA W scheme is superior to these competitors regardless of the size of the shift in the location parameter. Real-life data are used to illustrate the implementation and application of the new monitoring schemes.en_US
dc.description.departmentStatisticsen_US
dc.description.librariandm2022en_US
dc.description.urihttp://www.plosone.orgen_US
dc.identifier.citationLetshedi, T.I., Malela-Majika, J.-C. & Shongwe, S.C. (2022) New extended distribution-free homogenously weighted monitoring schemes for monitoring abrupt shifts in the location parameter. PLoS One 17(1): e0261217. https://doi.org/10.1371/journal.pone.0261217.en_US
dc.identifier.issn1932-6203 (online)
dc.identifier.other10.1371/journal.pone.0261217
dc.identifier.urihttps://repository.up.ac.za/handle/2263/88165
dc.language.isoenen_US
dc.publisherPublic Library of Scienceen_US
dc.rights© 2022 Letshedi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.en_US
dc.subjectStatistical distributionsen_US
dc.subjectMonte Carlo methoden_US
dc.subjectData miningen_US
dc.subjectProbability distributionen_US
dc.subjectNormal distributionen_US
dc.subjectChartsen_US
dc.subjectIndustrial processesen_US
dc.subjectTest statisticsen_US
dc.subjectHomogeneously weighted moving average (HWMA)en_US
dc.subjectExponentially weighted moving average (EWMA)en_US
dc.subjectCumulative sum (CUSUM)en_US
dc.titleNew extended distribution-free homogenously weighted monitoring schemes for monitoring abrupt shifts in the location parameteren_US
dc.typeArticleen_US

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