Abstract:
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For many years, control charts have been utilized to monitor processes, improve quality, and increase profitability. However, the body of literature tilts overwhelmingly toward charts monitoring normally distributed processes. In practice, the underlying distribution of a process may not follow a normal distribution, and many of those techniques may not be most effective. Mukherjee, McCracken, and Chakraborti (2015) suggested three control charts for simultaneous monitoring of the location and scale parameters for processes following the shifted exponential distribution. This study examines their proposed, "Shifted Exponential Maximum Likelihood Estimator - Max Chart," (SEMLE-max) and suggests utilization of penalized maximum likelihood estimators (MLE) instead of traditional MLE's because of unbiasedness and lower variability. The new chart, the Penalized SEMLE-max chart is constructed using similar methodology, and simulated data is used to compare average run lengths of the proposed chart to those obtained by the original chart.
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