Multivariate Models and the First Four Moments

被引：0
|
作者：
Nordhausen, Klaus [1 ]
Oja, Hannu [1 ]
Ollila, Esa [2 ]
机构：
[1] Univ Tampere, Tampere Sch Publ Hlth, Tampere, Finland
[2] Aalto Univ, Sch Sci & Technol, Dept Signal Proc & Acoust, Helsinki, Finland
来源：
NONPARAMETRIC STATISTICS AND MIXTURE MODELS: A FESTSCHRIFT IN HONOR OF THOMAS P HETTMANSPERGER | 2011年
关键词：
Elliptical distribution; Independent components analysis; Multivariate skewness and kurtosis; Skew-elliptical distribution; LOCATION;
D O I：
暂无
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Several extensions of the multivariate normal model have been shown to be useful in practical data analysis. Therefore, tools to identify which model might be appropriate for the analysis of a real data set are needed. This paper suggests the simultaneous use of two location and two scatter functionals to obtain multivariate descriptive measures for multivariate location, scatter, skewness and kurtosis, and shows how these measures can be used to distinguish among a wide range of models that extend the multivariate normal model. The method is demonstrated with examples on simulated and real data.
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页码：267 / 287
页数：21
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