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Quality Technology & Quantitative Management Volume 16, 2019 - Issue 1
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Articles

A robust multivariate sign control chart for detecting shifts in covariance matrix under the elliptical directions distributions

Wenjuan LiangSchool of Mathematics and Statistics, Huangshan University, Huangshan, China.;School of Statistics, East China Normal University, Shanghai, China.View further author information
,
Dongdong XiangSchool of Statistics, East China Normal University, Shanghai, China.Correspondence[email protected]
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,
Xiaolong PuSchool of Statistics, East China Normal University, Shanghai, China.View further author information
,
Yan LiSchool of Statistics, East China Normal University, Shanghai, China.View further author information
&
Lingzhu JinSchool of Statistics, East China Normal University, Shanghai, China.;Department of Financial Data Technology, Shanghai DZH Limited, Shanghai, China.View further author information
Pages 113-127 | Accepted 17 Aug 2017, Published online: 12 Sep 2017

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M. S. Barale & D. T. Shirke. (2023) A control chart based on data depth for monitoring the variability in a multivariate process. Communications in Statistics - Simulation and Computation 0:0, pages 1-15.
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Mohsen Ebadi, Shojaeddin Chenouri, Dennis K. J. Lin & Stefan H. Steiner. (2022) Statistical monitoring of the covariance matrix in multivariate processes: A literature review. Journal of Quality Technology 54:3, pages 269-289.
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Hongying Jing, Jian Li & Kaizong Bai. (2022) Directional monitoring and diagnosis for covariance matrices. Journal of Applied Statistics 49:6, pages 1449-1464.
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Azaz Ahmed, Aamir Sanaullah & Muhammad Hanif. (2020) A robust alternate to the HEWMA control chart under non-normality. Quality Technology & Quantitative Management 17:4, pages 423-447.
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Abdul Haq & Michael B. C. Khoo. (2020) Multivariate process dispersion monitoring without subgrouping. Journal of Applied Statistics 47:9, pages 1652-1675.
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