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초록·키워드 목차

Hotelling"s (1947) chi-square chart has a disadvantage in that it does not react sensitively to very small changes in the process, so we propose a multivariate chart with rules of various auxiliary runs to compensate for this. We use the finite Markov chain imbedding method and the rule of an auxiliary run to find the run-lengths distribution in a multivariate control chart. In this paper, we find the run-lengths distribution for the covariance matrix. At this time, it is assumed that the correlation coefficient according to the change of the covariance matrix does not change. We try to help evaluate the performance of the control chart by indicating not only the average run length but also the quartile. #Average run length #Markov chain imbedding #multivariate control charts #run-length distribution

Abstract
1. Introduction
2. Finite Markov chain imbedding
3. Run-length distribution of multivariate control charts
4. Conclusion
References

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