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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.7
- 수록면
- 937 - 945 (9page)
이용수
초록· 키워드
오류제보하기
The purpose of using a control chart is to detect any change that occurs in the process. When control charts are used to monitor processes, we want to identify this changes as quickly as possible. Many problems in quality control involve a vector of observations of several characteristics rather than a single characteristic. Multivariate CUSUM or EWMA charts have been developed to address the problem of monitoring covariance matrix or the joint monitoring of mean vector and covariance matrix. However, control charts tend to work poorly when we use the highly correlatted variables. In order to overcome it, Hawkins (1991) proposed the use of regression adjustment variables. In this paper, to monitor covariance matrix, we investigate the performance of MEWMA-type control charts with and without the use of regression adjusted variables.
목차
Abstract
1. Introduction
2. Definition of control charts
3. Numerical results
4. Summary and concluding remark
References
참고문헌 (12)
참고문헌 신청
Choi, H. Y. and Cho, G. Y. (2016). Multivariate CUSUM control charts for monitoring the covariance matrix. Journal of the Korean Data & Information Science Society, 27, 539-548.
Hawkins, D. M. (1991). Multivariate quality control based on regression-adjusted variables. Technometrics, 33, 61-75
Hawkins, D. M. (1993). Regression adjustment for variables in multivariate quality control. Journal of Quality Technology, 25, 170-182.
Hotelling, H. (1947). Multivariate quality control-illustrated by the air testing of sample bombsights in techniques of statistical analysis edited by C. Eisenhart, M.W. Hastay, and W.A. Wallis, McGraw- Hill, New York.
Jeong, J. I. and Cho, G. Y. (2012a). Multivariate Shewhart control charts for monitoring the vatiance-covariance matrix. Journal of the Korean Data & Information Science Society, 23, 617-626.
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