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http://dx.doi.org/10.29220/CSAM.2020.27.1.065

Geometric charts with bootstrap-based control limits using the Bayes estimator  

Kim, Minji (Department of Applied Statistics, Chung-Ang University)
Lee, Jaeheon (Department of Applied Statistics, Chung-Ang University)
Publication Information
Communications for Statistical Applications and Methods / v.27, no.1, 2020 , pp. 65-77 More about this Journal
Abstract
Geometric charts are effective in monitoring the fraction nonconforming in high-quality processes. The in-control fraction nonconforming is unknown in most actual processes; therefore, it should be estimated using the Phase I sample. However, if the Phase I sample size is small the practitioner may not achieve the desired in-control performance because estimation errors can occur when the parameters are estimated. Therefore, in this paper, we adjust the control limits of geometric charts with the bootstrap algorithm to improve the in-control performance of charts with smaller sample sizes. The simulation results show that the adjustment with the bootstrap algorithm improves the in-control performance of geometric charts by controlling the probability that the in-control average run length has a value greater than the desired one. The out-of-control performance of geometric charts with adjusted limits is also discussed.
Keywords
Bayes estimator; bootstrap algorithm; control limits; geometric chart; statistical process control;
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Times Cited By KSCI : 2  (Citation Analysis)
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