Model-based control chart for autoregressive and correlated data

Elvira N. Loredo, Duangporn Jearkpaporn, Connie M. Borror

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


In recent years, statistical process control for autocorrelated processes has received a great deal of attention. This is due in part to the improvements in measurement and data collection that allow processes to be sampled at higher frequency rates and, hence, data autocorrelation. A method for monitoring autocorrelated processes based on regression adjustment is presented in this paper. The performance of the residual-based control chart in terms of the average run length is compared to observation-based control charts via Monte Carlo simulations. In general, the observation-based control charts perform very poorly when data are correlated over time. Under the assumption that the model is correct, the residual-based control charts are superior for all cases considered here. This suggests using a residual-based control chart to detect the mean shift. This is recommended particularly for chemical processes where there are often cascade processes with several inputs but only a few outputs, and where many of the variables are highly autocorrelated.

Original languageEnglish (US)
Pages (from-to)489-496
Number of pages8
JournalQuality and Reliability Engineering International
Issue number6
StatePublished - Nov 2002


  • Autocorrelated processes
  • Cascade processes
  • Model-based control chart
  • Residual-based control chart

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research


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