This paper develops a distribution-free (or nonparametric) Shewhart-type statistical quality control chart for detecting a broad\r\nchange in the probability distribution of a process. The proposed chart is designed for grouped observations, and it requires the\r\navailability of a reference (or training) sample of observations taken when the process was operating in-control. The charting\r\nstatistic is a modified version of the two-sample Kolmogorov-Smirnov test statistic that allows the exact calculation of the conditional\r\naverage run length using the binomial distribution. Unlike the traditional distribution-based control charts (such as the\r\nShewhart X-Bar), the proposed chart maintains the same control limits and the in-control average run length over the class of all\r\n(symmetric or asymmetric) continuous probability distributions. The proposed chart aims at monitoring a broad, rather than a\r\none-parameter, change in a process distribution. Simulation studies show that the chart is more robust against increased skewness\r\nand/or outliers in the process output. Further, the proposed chart is shown to be more efficient than the Shewhart X-Bar chart\r\nwhen the underlying process distribution has tails heavier than those of the normal distribution.
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