• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.345-355
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• 2014

The number of nonconformities in a unit is commonly modeled by a Poisson distribution. As an extension of a Poisson distribution, a zero-inflated Poisson(ZIP) process can be used to fit count data with an excessive number of zeroes. In this paper, we propose a generalized likelihood ratio(GLR) chart to monitor shifts in the two parameters of the ZIP process. We also compare the proposed GLR chart with the combined cumulative sum(CUSUM) chart and the single CUSUM chart. It is shown that the overall performance of the GLR chart is comparable with CUSUM charts and is significantly better in some cases where the actual directions of the shifts are different from the pre-specified directions in CUSUM charts.

• Communications for Statistical Applications and Methods
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• v.21 no.1
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• pp.69-80
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• 2014

This paper considers the problem of monitoring the mean of a normally distributed process variable when the objective is to effectively detect both a sustained shift and a linear drift. The design and application of a generalized likelihood ratio (GLR) chart for simultaneously monitoring a sustained shift and a linear drift are evaluated. The GLR chart has the advantage that when we design this chart, we do not need to specify the size of the parameter change. The performance of the GLR chart is compared with that of other control charts, such as the standard cumulative sum (CUSUM) charts and the cumulative score (CUSCORE) charts. And we compare the proposed GLR chart with the GLR charts designed for monitoring only a sustained shift and for monitoring only a linear drift. Finally, we also compare the proposed GLR chart with the chart combinations. We show that the proposed GLR chart has better overall performance for a wide range of shift sizes and drift rates relative to other control charts, when a special cause produces a sustained shift and/or a linear drift in the process mean.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.787-796
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• 2014

Situations where sample size is not constant are common when monitoring a process with Poisson count data. In this paper, we propose a generalized likelihood ratio(GLR) control chart to detect shifts in the Poisson rate when the sample size varies. The performance of the proposed GLR chart is compared with the performance of several cumulative sum(CUSUM) type charts. It is shown that the overall performance of the GLR chart is comparable with CUSUM type charts and is significantly better in cases where the actual value of the shift is different from the pre-specified value in CUSUM type charts.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.647-663
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• 2017

Weibull distribution is a popular distribution for modeling lifetimes because it reflects the characteristics of failure adequately and it models either increasing or decreasing failure rates simply. It is a standard method of the lifetimes test to wait until all samples failed; however, censoring can occur due to some realistic limitations. In this paper, we propose a generalized likelihood ratio (GLR) chart to monitor changes in the scale parameter for type I right-censored Weibull lifetime data. We also compare the performance of the proposed GLR chart with two CUSUM charts proposed earlier using average run length (ARL). Simulation results show that the Weibull GLR chart is effective to detect a wide range of shift sizes when the shape parameter and sample size are large and the censoring rate is not too high.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.509-521
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• 2008

Two control charts are usually required to monitor both the process mean and variance. In this paper, we introduce control procedures for jointly monitoring the process mean and variance with one control chart, and investigate efficiency of the introduced charts by comparing with the combined two EWMA charts. Our numerical results show that the GLR chart, the Omnibus EWMA chart, and the Interval chart have good ARL properties for simultaneous changes in the process mean and variance.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.357-365
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• 2010

This paper considers the integrated process control procedure for detecting special causes in an IMA(1,1) noise process that is being adjusted using a minimum mean squared error adjustment. As a SPC procedure, we use a GLR chart for detecting special causes whose effects are the sustained shift or the sustained drift in the process mean, and the sustained shift in the process variance. For the design of the GLR chart, we derive expressions for the control limit which accurately satisfies the given in-control ARL.