In constructing any control chart, the sample size and the number of samples taken are important issues. When destructive tests are applied to collect data, the sample size and the number of subsamples are usually limited. Lack of sufficient observations affects parameters estimates. Re-sampling techniques are always suggested to increase the accuracy of parameters estimates. Also, the presence of outlying observations severely affect re-sampling and the estimates of the underlying distribution parameters. The limited sample size, limited number of subsamples and presence of outliers are more important in multivariate process analysis. In this study, bootstrap re-sampling technique and robust estimators of mean vector and variance-covariance matrix are simultaneously utilized to design a multivariate robust T 2 control chart to address the three problems in hand. The performance of the proposed robust T 2 control chart is evaluated and compared to the Hotelling T 2 control chart by means of average run length (ARL). Simulation results indicate that the suggested robust T 2 control chart outperforms the Hotelling T 2 in presence of outliers and similarly when there is no outlier. Design of robust control chart for processes generating auto-correlated data applying bootstrap re-sampling technique is an area for further research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.