A Distribution-Free Multivariate Control Chart

Abstract

Monitoring multivariate quality variables or data streams remains an important and challenging problem in statistical process control (SPC). Although the multivariate SPC has been extensively studied in the literature, designing distribution-free control schemes are still challenging and yet to be addressed well. This article develops a new nonparametric methodology for monitoring location parameters when only a small reference dataset is available. The key idea is to construct a series of conditionally distribution-free test statistics in the sense that their distributions are free of the underlying distribution given the empirical distribution functions. The conditional probability that the charting statistic exceeds the control limit at present given that there is no alarm before the current time point can be guaranteed to attain a specified false alarm rate. The success of the proposed method lies in the use of data-dependent control limits, which are determined based on the observations online rather than decided before monitoring. Our theoretical and numerical studies show that the proposed control chart is able to deliver satisfactory in-control run-length performance for any distributions with any dimension. It is also very efficient in detecting multivariate process shifts when the process distribution is heavy-tailed or skewed. Supplementary materials for this article are available online.

• Conditionally distribution-free
• Empirical distribution
• Nonparametric procedure
• Robustness
• Self-starting
• Statistical process control.

ACKNOWLEDGMENTS

The authors thank the editor, associate editor, and two anonymous referees for their many helpful comments that have resulted in significant improvements in the article. Chen was partially supported by Singapore AcRF Tier 1 funding #R-266-000-078-112. Zi and Zou were supported by the NNSF of China Grants 11622104, 11431006, 11131002, 11371202, 11271205 and Foundation for the Author of National Excellent Doctoral Dissertation of PR China 201232. Zou is the corresponding author.