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A characterization of multivariate normality through univariate projections

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  • Shao, Yongzhao
  • Zhou, Ming

Abstract

This paper introduces a new characterization of multivariate normality of a random vector based on univariate normality of linear combinations of its components.

Suggested Citation

  • Shao, Yongzhao & Zhou, Ming, 2010. "A characterization of multivariate normality through univariate projections," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2637-2640, November.
    • Handle: RePEc:eee:jmvana:v:101:y:2010:i:10:p:2637-2640
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    References listed on IDEAS

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    1. Sinz, Fabian & Gerwinn, Sebastian & Bethge, Matthias, 2009. "Characterization of the p-generalized normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 817-820, May.
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    Cited by:

    1. Jiang, Qing & Hušková, Marie & Meintanis, Simos G. & Zhu, Lixing, 2019. "Asymptotics, finite-sample comparisons and applications for two-sample tests with functional data," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 202-220.
    2. Vexler, Albert, 2020. "Univariate likelihood projections and characterizations of the multivariate normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    3. Cuesta-Albertos, J.A. & del Barrio, E. & Fraiman, R. & Matrán, C., 2012. "Letter to the editor," Journal of Multivariate Analysis, Elsevier, vol. 103(1), pages 161-162, January.
    4. Ming Zhou & Yongzhao Shao, 2014. "A powerful test for multivariate normality," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(2), pages 351-363, February.
    5. Sirao Wang & Jiajuan Liang & Min Zhou & Huajun Ye, 2022. "Testing Multivariate Normality Based on F -Representative Points," Mathematics, MDPI, vol. 10(22), pages 1-22, November.

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