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[학술저널]

  • 학술저널

O. Ikpotokin(Ambrose Alli University) I. U. Siloko(Edo University Iyamho)

DOI : 10.7232/iems.2019.18.3.315

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초록

In the manufacturing and service industries, multivariate statistical quality control charts are mostly used to determine whether a process is performing as intended or if there are some special causes resulting in the variation of an overall statistics. Normally, control charts are obtained under the assumption that the variable under study follows some form of parametric distribution. When this assumption is violated, the performance of such control chart often gives false alarm signals. To address this problem, the Multivariate Exponential Weighted Moving Average (MEWMA) control limits have been proposed in existing literature. This article focuses on reviewing this existing method with a view to developing a novel approach based on the use of bootstrap. Results from a performance study shows that the proposed method enables the setting of control limits that can enhance the easy detection of out of control signals.

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ABSTRACT
1. INTRODUCTION
2. THE NON-PARAMETRIC BOOTSTRAP MULTIVARIATE EXPONENTIALLY-WEIGHTED MOVING AVERAGE (BMEWMA) CONTROL LIMITS
3. APPLICATION TO NUMERICAL EXAMPLE
4. CONCLUSION
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