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• W2897601180 abstract "With rapid advances in sensing technology and data acquisition systems, high‐dimensional data appear in many settings. The high dimensionality presents a new challenge to the traditional tools in multivariate statistical process control, due to the “curse of dimensionality.” Various tests for mean vectors in high dimensional situations have been discussed recently; however, they have been rarely adapted to process monitoring. This paper develops a distribution‐free control chart based on interpoint distances for monitoring mean vectors in high‐dimensional settings. Other than the Euclidean distance, the family of Minkowski distance is used for discussion, which is a generalization of the former and other distances. The proposed approach is very general as it represents a class of distribution‐free control charts based on distances. Numerical results show that the proposed control chart is efficient in detecting mean shifts in both symmetric and heavy‐tailed distributions." @default.
• W2897601180 created "2018-10-26" @default.
• W2897601180 creator A5038361618 @default.
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• W2897601180 date "2018-06-01" @default.
• W2897601180 modified "2023-10-03" @default.
• W2897601180 title "A distribution-free control chart for monitoring high-dimensional processes based on interpoint distances" @default.
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• W2897601180 doi "https://doi.org/10.1002/nav.21809" @default.
• W2897601180 hasPublicationYear "2018" @default.
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