On the detection and estimation of changes in a process mean based on kernel estimators
Files
Date
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
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.