Multivariate skewness and Kurtosis for singular distributions

Multivariate skewness and Kurtosis for singular distributions

In multivariate analysis it is generally assumed that the observations are normally distributed. It was Mardia ([1] to [5]), who first introduced measures of multivariate skewness and kurtosis; these statistics are affine invariant and can be used for testing multivariate normality. Skewness and kur...

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Veröffentlicht in:Extracta mathematicae 1993, Vol.8 (2-3), p.98-101
Hauptverfasser: Sánchez, J. M, Ardanuy Albajar, Ramón
Format: Artikel
Sprache:eng ; spa
Schlagworte:
Análisis multivariante
Asimetría
Curtosis
Distribuciones singulares
Matrices
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Zusammenfassung:In multivariate analysis it is generally assumed that the observations are normally distributed. It was Mardia ([1] to [5]), who first introduced measures of multivariate skewness and kurtosis; these statistics are affine invariant and can be used for testing multivariate normality. Skewness and kurtosis tests remain among the most powerful, general and easy to implement. In this paper we show some properties of these statistics when population distribution is singular.
ISSN:0213-8743
2605-5686