A dynamic sampling for monitoring nonparametric multivariate processes

Abstract

In the context of big data, multivariate processes must often be monitored in a timely and accurate manner. Usually, the distribution of process variables is unknown. This paper proposes a new strategy for multivariate process monitoring when the distribution of a process variable is unknown. We address monitoring by means of a rank-based method that is completely nonparametric. We also discuss the optimal strategy of parameters. A simulation study demonstrates that the proposed method is efficient in detecting shifts for multivariate processes. A real data example is presented to illustrate the performance of the proposed method.

Keywords:

• Dynamic sampling
• Rank-based method
• Multivariate processes
• CUSUM
• Nonparametric scheme

Acknowledgments

The authors thank the editor and two anonymous referees for their many helpful comments that have resulted in significant improvements in the article.

Additional information

Funding

This work was supported by grants from the National Natural Science Foundation of China (Nos. 12075162, 71872146), the Sichuan Federation of Social Science Associations (No. SC20TJ016), the Sichuan Science and Technology Department (No. 2020YJ0357) and the VC&VR Key Lab of Sichuan Province.