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A nonparametric multivariate cumulative sum procedure for detecting shifts in all directions

Peihua Qiu & Douglas M. Hawkins

Journal of the Royal Statistical Society, Series D (The Statistician)2003https://doi.org/10.1111/1467-9884.00348article
ABDC A
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0.51

Abstract

Summary. The fairly limited range of tools for multivariate statistical process control generally rests on the assumption that the data vectors follow a multivariate normal distribution—an assumption that is rarely satisfied. We discuss detecting possible shifts in the mean vector of a multivariate measurement of a statistical process when the multivariate distribution of the measurement is non-Gaussian. A nonparametric cumulative sum procedure is suggested which is based both on the order information among the measurement components and on the order information between the measurement components and their in-control means. It is shown that this procedure is effective in detecting a wide range of possible shifts. Several numerical examples are presented to evaluate its performance. This procedure is also applied to a data set from an aluminium smelter.

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https://doi.org/https://doi.org/10.1111/1467-9884.00348

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@article{peihua2003,
  title        = {{A nonparametric multivariate cumulative sum procedure for detecting shifts in all directions}},
  author       = {Peihua Qiu & Douglas M. Hawkins},
  journal      = {Journal of the Royal Statistical Society, Series D (The Statistician)},
  year         = {2003},
  doi          = {https://doi.org/https://doi.org/10.1111/1467-9884.00348},
}

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A nonparametric multivariate cumulative sum procedure for detecting shifts in all directions

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0.51

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

F · citation impact0.48 × 0.4 = 0.19
M · momentum0.63 × 0.15 = 0.10
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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