Abstract:
Parametric control charts are very attractive and have been used in the industry for a very long time. However, in many applications the underlying process distribution is not known sufficiently to assume a specific distribution function. When the distributional assumptions underlying a parametric control chart are violated, the performance of the control chart could be potentially affected. Since robustness to departures from normality is a desirable property for control charts, this dissertation reports three separate papers on the development and evaluation of robust Shewhart-type control charts for both the univariate and multivariate cases. In addition, a statistical procedure is developed for detecting step changes in the mean of the underlying process given that Shewhart-type control charts are not very sensitive to smaller changes in the process mean. The estimator is intended to be applied following a control chart signal to aid in diagnosing root cause of change. Results indicate that methodologies proposed throughout this dissertation research provide robust in-control average run length, better detection performance than that offered by the traditional Shewhart control chart and/or the Hotelling's control chart, and meaningful change point diagnostic statistics to aid in the search for the special cause.
