Switching properties of CUSUM charts for controlling mean vector

Chang, Duk-Joon; Heo, Sunyeong

doi:10.7465/jkdi.2012.23.4.859

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Reynolds (1995) evaluated properties of variable sampling interval control charts. Chang and Heo (2012) investgated switching properties of CUSUM charts for controlling mean vector. Reynolds and Stoumbos (2001) studied on monitoring the process mean and variance using individual observations and variable sampling intervals.…”

Section: Properties Of Vsi Proceduresmentioning

confidence: 99%

Switching properties of bivariate Shewhart control charts for monitoring the covariance matrix

Gwon¹,

Cho²

2015

Journal of the Korean Data and Information Science Society

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A control chart is very useful in monitoring various production process. There are many situations in which the simultaneous control of two or more related quality variables is necessary. We construct bivariate Shewhart control charts based on the trace of the product of the estimated variance-covariance matrix and the inverse of the in-control matrix and investigate the properties of bivariate Shewart control charts with VSI procedure for monitoring covariance matrix in term of ATS (Average time to signal) and ANSW (Average number of switch) and probability of switch, ASI (Average sampling interval). Numerical results show that ATS is smaller than ARL. From examining the properties of switching in changing covariances and variances in Σ, ANSW values show that it does not switch frequently and does not matter to use VSI procedure.

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Section: Properties Of Vsi Proceduresmentioning

confidence: 99%

Switching properties of bivariate Shewhart control charts for monitoring the covariance matrix

Gwon¹,

Cho²

2015

Journal of the Korean Data and Information Science Society

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“…A multivariate extension of the EWMA control chart was presented by Lowry et al (1992). Multivariate control charts were studied by Cho (2010), Chang and Heo (2012). Most of the studies on multivariate control charts have been concentrated on monitoring mean vector of multivariate normal process.…”

Section: Introductionmentioning

confidence: 99%

Multivariate control charts for monitoring correlation coefficients in dispersion matrix

Chang¹,

Heo²

2012

Journal of the Korean Data and Information Science Society

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Multivariate control charts for effectively monitoring every component in the dispersion matrix of multivariate normal process are considered. Through the numerical results, we noticed that the multivariate control charts based on sample statistic Vi by Hotelling or Wi by Alt do not work effectively when the correlation coefficient components in dispersion matrix are increased. We propose a combined procedure monitoring every component of dispersion matrix, which operates simultaneously both control charts, a chart controlling variance components and a chart controlling correlation coefficients. Our numerical results show that the proposed combined procedure is efficient for detecting changes in both variances and correlation coefficients of dispersion matrix.

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Switching properties of CUSUM charts for controlling mean vector

Cited by 2 publications

References 8 publications

Switching properties of bivariate Shewhart control charts for monitoring the covariance matrix

Switching properties of bivariate Shewhart control charts for monitoring the covariance matrix

Multivariate control charts for monitoring correlation coefficients in dispersion matrix

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