Using vector autoregressive residuals to monitor multivariate processes in the presence of serial correlation
By Xia Pan, Jeffrey E. Jarrett. 2007
68 citations recorded by the source corpus.
Abstract Traditional literature on statistical quality control discusses separately multivariate control charts for independent processes and univariate control charts for autocorrelated processes. We extend univariate residual monitoring to the multivariate environment, and propose using vector autoregressive residuals (VAR) to monitor multivariate processes in the presence of serial correlation. We mathematically examine the effects of shifts in process parameters on the VAR residual chart, and give examples using data from a plastic production process. The results indicate the feasibility of VAR residual chart to achieve quality control and improvement.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/37fb9d999ed16572f7c245472e4bd473e675694f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
Citations in the corpus
- Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes (2013, background): “Therefore, some other control charts based on residual have been developed for enhancing the performance of monitoring correlated processes (Testik, 2005; Pan and Jarrett, 2007).”
- A new multivariate gage R&R method for correlated characteristics (2013, background): “Some of these include: Chen et al. (2005), Villalobos et al. (2005), Yang and Rahim (2005), Chen et al. (2006), Pan (2007), Pan and Jarrett (2007), Machado and Costa (2008), Chen and Chen (2008), Psarakis (2011), Boone and Chakraborti (2012), and Wang (2012).”