Robust g-type quality control charts for monitoring nonconformities

Abstract

The g-type quality control charts based on the geometric distribution are commonly used to monitor the number of conforming cases between the two consecutive appearances of nonconformities. The process parameter in these charts is generally estimated based on conventional methods such as the maximum likelihood and minimum variance unbiased estimators, whereas these are very sensitive to outliers and thus could result in severe bias for obtaining the control limits of the charts. To overcome this issue, based on the memoryless property of the geometric distribution along with truncation of a distribution, we develop two robust estimators for the process parameter. Simulation studies and real-data applications illustrate that in terms of the accuracy of the parameter estimation, the relative efficiency, and the run length analysis, the proposed methods outperform existing ones when the data contain outliers.