Monitoring variability of autocorrelated processes using standardized time series variance estimators

Authors:
Seong-Hee Kim
Affiliations:
Georgia Institute of Technology, Atlanta, GA
Venue:
Proceedings of the 38th conference on Winter simulation
Year:
2006

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Abstract

We consider the problem of monitoring variability of autocorrelated processes. This paper combines variance estimation techniques from the simulation literature with a statistical process control chart from statistical process control (SPC) literature. The proposed SPC method does not require any assumptions on the distribution of the underlying process and uses a variance estimate from each batch as a basic observation. The control limits of the chart are determined analytically. The proposed chart is tested using stationary processes with both normal and non-normal marginals.