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A Study on the Variance Estimation for a Stationary Process in SPC

Published

Author(s)

Nien F. Zhang

Abstract

Recently, statistical process control (SPC) methodologies have been developed to accommodate autocorrelated data. To construct control charts for stationary process data, the process variance needs to be estimated. For an independently identically distributed sequence of a random variable, the variance is usually estimated by the sample variance. For a weakly stationary process, different estimators of the process variance can be used. In this paper, comparisons of estimators of the process variance are made based on the criterion of minimum squared error.
Proceedings Title
Proceedings of the American Statistical Association

Keywords

Burg estimator, least squares estimator, mean squared error, statistical process control, time series

Citation

Zhang, N. (2003), A Study on the Variance Estimation for a Stationary Process in SPC, Proceedings of the American Statistical Association (Accessed May 25, 2024)

Issues

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Created February 1, 2003, Updated February 17, 2017