• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.97-103
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• 2003

Shewhart-type control charts have historically been used for attribute data, though they have ARL biased property and even are unable to detect the improvement of a process with some process parameters. So far most efforts have been made to improve the performance of attribute control charts in terms of faster detection of special causes without increasing the rates of false alarm. In this paper, control limits are proposed that yield an ARL (nearly) unbiased chart for attributes. Optimal design is also proposed for attribute control charts under a natural sense of criterion.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.715-723
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• 1997

Multivariate control statistics to simultaneously monitor both means and variances for several quality variables under multivariate normal process are proposed. Performances of the proposed multivariate charts are evaluated in terms of average run length(ARL). Multivariate Shewhart chart is also proposed to compare the performances of multivariate exponentially weighted moving average(EWMA) charts. A numerical comparison shows that multivariate EWMA charts are more efficient than multivariate Shewhart chart for small and moderate shifts and multivariate EWMA scheme based on accumulate-combine approach is more efficient than corresponding multivariate EWMA chart based on combine-accumulate approach.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.467-476
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• 2005

Multivariate control charts for monitoring mean vector of several related quality variables with combine-accumulate approach and accumulate-combine apprach were investigated. Shewhart chart is also proposed to compare the performances of CUSUM and EWMA charts. Numerical comparisons show that CUSUM and EWMA charts are more efficient than Shewhart chart for small or moderate shifts, and multivariate charts based on accumulate- combine approach is more efficient than corresponding multivariate charts based on combine-accumulate approach.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.1045-1053
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• 2003

Markovian EWMA control chart for simultaneously monitoring mean vector of the several correlated quality characteristics is investigated. Properties of multivariate Shewhart chart and EWMA chart are evaluated for matched FSI (fixed sampling interval) and VSI(variable sampling interval) scheme. We obtained VSI EWMA chart is more efficient than Shewhart chart for small or moderate shifts. And, we obtained stablized numerical results with Markov chain method when the number of transient state is greater than 100.

• Journal of the Korean Statistical Society
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• v.33 no.2
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• pp.219-231
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• 2004

Process adjustment is a complimentary tool to process monitoring in process control. Although original intention of process adjustment is not identifying a special cause, detection and elimination of special causes may lead to significant process improvement. In this paper, we examine the impact of special causes on process adjustment. The bias in the adjusted output process is derived for each type of special causes, and average run length (ARL) of the Shewhart chart applied to the adjusted output is computed for each special cause types. Numerical results are illustrated for the ARL of the Shewhart chart, thereupon seriousness of special causes on process adjustment is evaluated for each type of special causes.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.3
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• pp.308-317
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• 2008

This research investigates economic characteristics of 2 of 2 runs rules under the Shewhart $\bar{X}$ control chart scheme. A Markov chain approach is employed in order to calculate the in-control average run length (ARL) and the average length of analysis cycle. States of the process are defined according to the process conditions at sampling time and transition probabilities are derived from the state definitions. A steady state cost function is constructed based on the Lorezen and Vance(1986) model. Numerical examples show that 2 of 2 runs rules are economically superior to the Shewhart $\bar{X}$ chart in many cases.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.1027-1034
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• 2015

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. When the joint distribution of the process variables is multivariate normal, multivariate Shewhart control charts using the function of the maximum likelihood estimator for monitoring the dispersion matrix are considered for the simultaneous monitoring of the dispersion matrix. The performances of the multivariate Shewhart control charts based on the proposed control statistic are evaluated in term of average run length (ARL). The performance is investigated in three cases, where the variances, covariances, and variances and covariances are changed respectively. The numerical results show that the performances of the proposed multivariate Shewhart control charts are not better than the control charts using the trace of the covariance matrix in the Jeong and Cho (2012) in terms of the ARLs.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.147-156
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• 2002

In this paper, we propose EWMA control charts using the life time data for the system with the constant failure rate, which were drawn from the fixed sampling interval without replacement(with replacement), and investigate the power of detection of EWMA by comparing ARL of EWMA control charts with one of Shewhart control charts.

• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.141-149
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• 2002

In this paper, we propose EWMA control charts using the life time data for the system with the constant failure rate, which were drawn from the fixed sampling interval without replacement (with replacement), and investigate the power of detection of EWMA by comparing ARL of EWMA control charts with one of Shewhart control charts.

• International Journal of Quality Innovation
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• v.7 no.3
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• pp.107-115
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• 2006

Control charts are important tools of statistical quality control. In 1956, Duncan first proposed the economic design of $\bar{x}-control$ charts to control normal process means and insure that the economic design control chart actually has a lower cost, compared with a Shewhart control chart. An moving average (MA) control chart is more effective than a Shewhart control chart in detecting small process shifts and is considered by some to be simpler to implement than the CUSUM. An economic design of MA control chart has also been proposed in 2005. The weaknesses to only the economic design are poor statistics because it dose not consider type I or type II errors and average time to signal when selecting design parameters for control chart. This paper provides a construction of an economic-statistical model to determine the optimal parameters of an MA control chart to improve economic design. A numerical example is employed to demonstrate the model's working and its sensitivity analysis is also provided.