Phase I Analysis for Autocorrelated Processes

Abstract

There has been recent interest in statistical process control for autocorrelated processes. Previous researchers have not distinguished models of autocorrelated common-cause variation from the actual behavior of baseline data. Standard estimators of common-cause parameters can be severely biased when assignable causes are present. A new estimation method given here overcomes this difficulty for the first-order autoregressive common-cause model. The method is illustrated with real data sets and assessed via simulation.

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