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.

• Autocorrelation
• Autoregressive Moving Average Model
• Common-Cause Variation
• Level Shifts
• Outliers
• Retrospective Analysis
• Structural Model
• Time Series Analysis
• Trends

Additional information

Notes on contributors

Russell A. Boyles

Dr. Boyles is Principal Consultant. He is a Member of ASQ. His email address is [email protected].