Step-Down Analysis for Changes in the Covariance Matrix and Other Parameters

Step-down diagnostic analysis to infer which means have changed (after detection of a shift in the mean vector) is familiar in multivariate statistical process control. We show how to include in the diagnostic analysis the elements of the covariance matrix and other higher order moments and give pra...

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Bibliographic Details
Published in:Journal of quality technology 2007-01, Vol.39 (1), p.66-84
Main Authors: Sullivan, Joe H., Stoumbos, Zachary G., Mason, Robert L., Young, John C.
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Control theory. Systems
Diagnosis of Shift
Exact sciences and technology
Individual Observations
Inventory control, production control. Distribution
Matrix
Multivariate analysis
Multivariate Gamma Distribution
Operational research and scientific management
Operational research. Management science
Parameter estimation
Process control. Computer integrated manufacturing
Process controls
Regime Shift
Reliability theory. Replacement problems
Retrospective Analysis
Statistical Process Control
Structural Change
Studies
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Summary:Step-down diagnostic analysis to infer which means have changed (after detection of a shift in the mean vector) is familiar in multivariate statistical process control. We show how to include in the diagnostic analysis the elements of the covariance matrix and other higher order moments and give practical examples. We use the asymptotic normality of maximum-likelihood estimators to classify the parameters that describe the prior- and post-shift distributions into those for which there is significant evidence of a shift and those for which such evidence is lacking. This diagnostic information can be useful in guiding the search for the cause(s) of the shift. For practical applications, the choice of parameterization may affect the ease of interpretation of the diagnostic results. It may be more informative to use standard deviations and correlation coefficients instead of variances and covariances, and this approach is illustrated in our analysis and examples. We also consider application to distributions other than the multivariate normal and give an example with a multivariate gamma distribution. Although illustrated with independent and identically distributed observations, the proposed methodology is more general.
ISSN:0022-4065
2575-6230
DOI:10.1080/00224065.2007.11917674