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Multivariate control charts based on regression-adjusted variables for covariance matrix
Journal of the Korean Data & Information Science Society 2017;28:937-45
Published online July 31, 2017
© 2017 Korean Data & Information Science Society.

Bumjun Kwon1 · Gyo-Young Cho2

12Department of Statistics, Kyungpook National University
Correspondence to: Gyo-Young Cho
Professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: gycho@knu.ac.kr
Received June 29, 2017; Revised July 11, 2017; Accepted July 15, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
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. How- ever, 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 vari- ables. 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.
Keywords : Average run length, covariance matrix, multivariate control chart, regression adjusted variables


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