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• W4313287642 startingPage "4786" @default.
• W4313287642 abstract "In addition to monitoring the Poisson mean rate with step shifts, increasing attention has been given to monitoring Poisson processes subject to linear trends. The exponentially weighted moving average (EWMA) control chart has been widely implemented to monitor normal processes, but it lacks investigation for detecting the Poisson mean change under a linear trend. In this paper, we analyze the performance of the EWMA chart by extending the Markov chain model from monitoring Poisson processes under a step shift to a Poisson process with linear drift. The results demonstrate that the proposed method is able to provide accurate average run length approximation, compared with the Monte Carlo simulation. Optimal design tables and sensitivity analysis are presented to facilitate the use of the EWMA chart in practice." @default.
• W4313287642 created "2023-01-06" @default.
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• W4313287642 date "2022-12-16" @default.
• W4313287642 modified "2023-09-26" @default.
• W4313287642 title "A Markov Chain Model for Approximating the Run Length Distributions of Poisson EWMA Charts under Linear Drifts" @default.
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• W4313287642 doi "https://doi.org/10.3390/math10244786" @default.
• W4313287642 hasPublicationYear "2022" @default.
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