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Multivariate Normal Plotting

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  • M. J. R. Healy

Abstract

Normal plotting techniques have proved very valuable for the detection of systematic non‐Normality and of outlying values. This paper describes an extension of these techniques to handle multivariate data.

Suggested Citation

  • M. J. R. Healy, 1968. "Multivariate Normal Plotting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 17(2), pages 157-161, June.
    • Handle: RePEc:bla:jorssc:v:17:y:1968:i:2:p:157-161
    DOI: 10.2307/2985678
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    Cited by:

    1. Bernard, Carole & Vanduffel, Steven, 2015. "A new approach to assessing model risk in high dimensions," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 166-178.
    2. Kirschstein, Thomas & Liebscher, Steffen & Becker, Claudia, 2013. "Robust estimation of location and scatter by pruning the minimum spanning tree," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 173-184.
    3. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
    4. Liang, Jia-Juan & Bentler, Peter M., 1999. "A t-distribution plot to detect non-multinormality," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 31-44, March.
    5. P. Navarro-Esteban & J. A. Cuesta-Albertos, 2021. "High-dimensional outlier detection using random projections," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 908-934, December.
    6. Tsung-I Lin & Pal Wu & Geoffrey McLachlan & Sharon Lee, 2015. "A robust factor analysis model using the restricted skew- $$t$$ t distribution," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 510-531, September.
    7. Seokho Lee & Marc G. Genton & Reinaldo B. Arellano-Valle, 2010. "Perturbation of Numerical Confidential Data via Skew-t Distributions," Management Science, INFORMS, vol. 56(2), pages 318-333, February.
    8. Liang, Jiajuan & Pan, William S.Y. & Yang, Zhen-Hai, 2004. "Characterization-based Q-Q plots for testing multinormality," Statistics & Probability Letters, Elsevier, vol. 70(3), pages 183-190, December.
    9. Fang, Kai-Tai & Li, Run-Ze & Liang, Jia-Juan, 1998. "A multivariate version of Ghosh's T3-plot to detect non-multinormality," Computational Statistics & Data Analysis, Elsevier, vol. 28(4), pages 371-386, October.
    10. Wan-Lun Wang & Min Liu & Tsung-I Lin, 2017. "Robust skew-t factor analysis models for handling missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 649-672, November.
    11. Takafumi Isogai, 1989. "On using influence functions for testing multivariate normality," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 41(1), pages 169-186, March.
    12. Agnieszka Wnęk & Dawid Kudas & Premysl Stych, 2021. "National Level Land-Use Changes in Functional Urban Areas in Poland, Slovakia, and Czechia," Land, MDPI, vol. 10(1), pages 1-16, January.

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