Robust g-type quality control charts for monitoring nonconformities. (December 2021)

• Record Type: Journal Article
• Authors: Park, Chanseok; Ouyang, Linhan; Wang, Min
• Is Part Of: Computers & industrial engineering. Volume 162(2021)
• Publication Date: 2021-12
• Subjects: Average run length -- Data contamination -- Empirical distribution -- Maximum likelihood estimates -- Relative efficiency; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107765
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Introduction of two robust estimators for the process parameter of the geometric distribution. Introduction of robust g-type control charts for monitoring adverse events. Using simulation to calculate relative efficiency and the average run length. Useful for both researchers and practitioners. 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.