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Rejoinder on: Tests for multivariate normality—a critical review with emphasis on weighted $$L^2$$ L 2 -statistics

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  • Bruno Ebner

    (Karlsruhe Institute of Technology (KIT))

  • Norbert Henze

    (Karlsruhe Institute of Technology (KIT))

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  • Bruno Ebner & Norbert Henze, 2020. "Rejoinder on: Tests for multivariate normality—a critical review with emphasis on weighted $$L^2$$ L 2 -statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 911-913, December.
    • Handle: RePEc:spr:testjl:v:29:y:2020:i:4:d:10.1007_s11749-020-00744-w
    DOI: 10.1007/s11749-020-00744-w
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    References listed on IDEAS

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    1. Tomoya Yamada & Megan Romer & Donald Richards, 2015. "Kurtosis tests for multivariate normality with monotone incomplete data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 532-557, September.
    2. L. Baringhaus & B. Ebner & N. Henze, 2017. "The limit distribution of weighted $$L^2$$ L 2 -goodness-of-fit statistics under fixed alternatives, with applications," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(5), pages 969-995, October.
    3. Norbert Henze & Jaco Visagie, 2020. "Testing for normality in any dimension based on a partial differential equation involving the moment generating function," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(5), pages 1109-1136, October.
    4. Ebner, Bruno, 2012. "Asymptotic theory for the test for multivariate normality by Cox and Small," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 368-379.
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    Cited by:

    1. Alfonso García-Pérez, 2021. "New Robust Cross-Variogram Estimators and Approximations of Their Distributions Based on Saddlepoint Techniques," Mathematics, MDPI, vol. 9(7), pages 1-21, April.
    2. Jurgita Arnastauskaitė & Tomas Ruzgas & Mindaugas Bražėnas, 2021. "A New Goodness of Fit Test for Multivariate Normality and Comparative Simulation Study," Mathematics, MDPI, vol. 9(23), pages 1-20, November.
    3. Wanfang Chen & Marc G. Genton, 2023. "Are You All Normal? It Depends!," International Statistical Review, International Statistical Institute, vol. 91(1), pages 114-139, April.
    4. Yiwen Cao & Jiajuan Liang & Longhao Xu & Jiangrui Kang, 2024. "Testing Multivariate Normality Based on Beta-Representative Points," Mathematics, MDPI, vol. 12(11), pages 1-16, May.
    5. Bruno Ebner & Norbert Henze & Simos Meintanis, 2024. "A unified approach to goodness-of-fit testing for spherical and hyperspherical data," Statistical Papers, Springer, vol. 65(6), pages 3447-3475, August.
    6. Chen, Feifei & Jiménez–Gamero, M. Dolores & Meintanis, Simos & Zhu, Lixing, 2022. "A general Monte Carlo method for multivariate goodness–of–fit testing applied to elliptical families," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    7. Alfonso García-Pérez, 2022. "On Robustness for Spatio-Temporal Data," Mathematics, MDPI, vol. 10(10), pages 1-17, May.
    8. Jianqing Fan & Weining Wang & Yue Zhao, 2024. "Conditional nonparametric variable screening by neural factor regression," Papers 2408.10825, arXiv.org.

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