Browsing by Author "Yeh, Arthur B."

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A control chart for the lognormal standard deviation
(2018-01-01)
Huang, Wei-Heng
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Yeh, Arthur B.
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Wang, Hsiuying
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統計學研究所
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Institute of Statistics
Among the statistical process control techniques, the control chart has been proven to be effective in process monitoring. The Shewhart S-chart is one of the most commonly used control charts for monitoring the process variability based on the assumption that the distribution of the quality characteristic to be monitored is normal. However, in real applications, many process distributions may follow a positively skewed distribution such as the lognormal distribution. In this study, a control chart for monitoring the standard deviation of a lognormal distribution is presented. The simulation results show that the proposed chart is more effective than the existing charts when the true value of the scale parameter of the lognormal distribution is large. A real example is used to demonstrate how the proposed chart can be applied in practice.
Control Charts for the Lognormal Mean
(2016-06)
Huang, Wei-Heng
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Wang, Hsiuying
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Yeh, Arthur B.
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統計學研究所
;
Institute of Statistics
Among a set of tools that form the core of statistical process control, statistical control charts are most commonly used for controlling, monitoring, and improving processes. The conventional control charts are based on the assumption that the distribution of the quality characteristic to be monitored follows the normal distribution. However, in real applications, many process distributions may follow a positively skewed distribution such as the lognormal distribution. In this study, we discuss the construction of several control charts for monitoring the mean of the lognormal distribution. A real example is used to demonstrate how these charts can be applied in practice. Copyright (c) 2015 John Wiley & Sons, Ltd.
Effective Control Charts for Monitoring Multivariate Process Dispersion
(2012-06-01)
Yen, Chia-Ling
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Shiau, Jyh-Jen Horng
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Yeh, Arthur B.
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統計學研究所
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Institute of Statistics
When monitoring process dispersion, it is common to pay more attention to dispersion increases than to decreases for practical reasons. Nonetheless, it is also important to detect dispersion decreases for two reasons: (i) it deserves further investigations as to why the process has improved; and (ii) if the process has changed, the settings of the control chart would need to be adjusted for effective future monitoring. In this paper, we first propose an effective control chart for detecting multivariate dispersion decreases in phase II process monitoring, which is constructed using the same approach as that of the one-sided likelihood-ratio-test-based multivariate chart proposed recently in the literature for detecting dispersion increases. We then discuss a combined charting scheme by combining these two one-sided charts for detecting either dispersion increases or decreases. Comparative simulation studies show that the proposed combined control charting scheme outperforms several existing two-sided control charts in terms of the average run length when the process dispersion indeed increases or decreases. Two real-life examples are presented to demonstrate the applicability of the proposed charts. Copyright (c) 2011 John Wiley & Sons, Ltd.
The density control chart: a general approach for constructing a single chart for simultaneously monitoring multiple parameters
(2012)
Chen, Hung-Chia
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Yeh, Arthur B.
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Yen, Chia-Ling
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Chen, Lin-An
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統計學研究所
"The majority of the existing literature on simultaneous control charts, i.e. control charting mechanisms that monitor multiple population parameters such as mean and variance on a single chart, assume that the process is normally distributed. In order to adjust and maintain the overall type-I error probability, these existing charts rely largely on the property that the sample mean and sample variance are independent under the normality assumption. Furthermore, the existing charting procedures cannot be readily extended to non-normal processes. In this article, we propose and study a general charting mechanism which can be used to construct simultaneous control charts for normal and non-normal processes. The proposed control chart, which we call the density control chart, is essentially based on the premise that if a sample of observations is from an in-control process, then another sample of observations is no less likely to be also from the in-control process if the likelihood of the latter is no less than the likelihood of the former. The density control chart is developed for normal and non-normal processes where the distribution of the plotting statistic of the density control chart can be explicitly derived. Real examples are given and discussed in these cases. We also discuss how the density control chart can be constructed in cases when the distribution of the plotting statistic cannot be determined. A discussion of potential future research is also given."