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Control Charts for Detecting Linear Drifts in Multivariate Process Mean and Covariance Matrix

Runzi Liao et al.

Applied Stochastic Models in Business and Industry2026https://doi.org/10.1002/asmb.70088article
ABDC B
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0.50

Abstract

Statistical process control (SPC) charts are widely used in various fields to detect distributional changes in sequential processes. Traditional SPC charts are primarily designed to identify abrupt changes in process parameters, such as sudden shifts in the mean or variance. However, many real‐world applications involve gradual changes over time, commonly referred to as drifts. This paper develops three change‐point detection charts for identifying linear drifts in the mean vector, the covariance matrix, and both simultaneously in a multivariate process. The proposed charts are constructed based on the generalized likelihood ratio statistic and change‐point detection techniques. These methods do not require pre‐specification of procedure parameters and provide an estimate of the change‐point location once a signal is given. Numerical studies demonstrate that the proposed charts are more effective in detecting linear drifts in the process mean and/or covariance matrix compared to conventional control charts designed for detecting abrupt changes.

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https://doi.org/https://doi.org/10.1002/asmb.70088

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@article{runzi2026,
  title        = {{Control Charts for Detecting Linear Drifts in Multivariate Process Mean and Covariance Matrix}},
  author       = {Runzi Liao et al.},
  journal      = {Applied Stochastic Models in Business and Industry},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/asmb.70088},
}

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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