Year: 2006
Type: article
Abstract: This paper develops a new multivariate control charting method for vector autocorrelated and serially correlated processes. The main idea is to propose a Bayesian multivariate local level model, which is a generalization of the Shewhart–Deming model for autocorrelated processes, in order to provide the predictive error distribution of the process and then to apply a univariate modified exponentially weighted moving average (EWMA) control chart to the logarithm of the Bayes' factors of the predictive error density versus the target error density. The resulting chart is proposed as capable of dealing with both the non‐normality and the autocorrelation structure of the log Bayes' factors. The new control charting scheme is general in application and it has the advantage of controlling simultaneously not only the process mean vector and the dispersion covariance matrix, but also the entire target distribution of the process. Two examples of London Metal Exchange data and of production time series data illustrate the capabilities of the new control chart. Copyright © 2006 John Wiley & Sons, Ltd.
DOI: 10.1002/qre.807
Author: K. Triantafyllopoulos
Institution: University of Sheffield
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2121773979