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Cluster-based artificial contrasts for inhomogeneously distributed data with an indicator variable

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  • Wook-Yeon Hwang

Abstract

Multivariate statistical process control is used for simultaneously monitoring several process variables. The original artificial contrasts (AC) are very useful for monitoring inhomogeneously distributed data with an indicator variable. The cluster-based AC improve it by considering separated clusters, respectively. Then the artificial data used for the AC overlap each cluster. Numerical experiments show that our method outperforms existing methods in terms of Type-II error rate.

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  • Wook-Yeon Hwang, 2016. "Cluster-based artificial contrasts for inhomogeneously distributed data with an indicator variable," International Journal of Production Research, Taylor & Francis Journals, vol. 54(17), pages 5045-5055, September.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:17:p:5045-5055
    DOI: 10.1080/00207543.2015.1075667
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    References listed on IDEAS

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    1. Seoung Kim & Thuntee Sukchotrat & Sun-Kyoung Park, 2011. "A nonparametric fault isolation approach through one-class classification algorithms," IISE Transactions, Taylor & Francis Journals, vol. 43(7), pages 505-517.
    2. F L Chen & C H Yeh, 2011. "Economic statistical design for x-bar control charts under non-normal distributed data with Weibull in-control time," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 750-759, April.
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