Cluster-based artificial contrasts for inhomogeneously distributed data with an indicator variable

Abstract

Multivariate statistical process control is used for simultaneously monitoring several process variables. The original artificial contrasts (AC) are very useful for monitoring inhomogeneously distributed data with an indicator variable. The cluster-based AC improve it by considering separated clusters, respectively. Then the artificial data used for the AC overlap each cluster. Numerical experiments show that our method outperforms existing methods in terms of Type-II error rate.

Keywords:

• multivariate statistical process control
• inhomogeneously distributed data
• artificial contrasts
• cluster
• random forests

Acknowledgements

This author thanks the editor and referees for their suggestions, which improved the presentation of the paper. He also thanks Professor George Runger for his help in reviewing this paper.

Disclosure statement

No potential conflict of interest was reported by the author.