The multivariate split normal distribution and asymmetric principal components analysis

被引：20
|
作者：
Villani, M
Larsson, R
机构：
[1] Stockholm Univ, Sveriges Riksbank, Div Res, SE-10337 Stockholm, Sweden
[2] Stockholm Univ, Dept Stat, SE-10337 Stockholm, Sweden
[3] Uppsala Univ, Dept Informat Sci, Uppsala, Sweden
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2006年 / 35卷 / 06期
关键词：
Bayesian inference; estimation; maximum likelihood; multivariate analysis; skewness; statistical distribution;
D O I：
10.1080/03610920600672252
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：
The multivariate split normal distribution extends the usual multivariate normal distribution by a set of parameters which allows for skewness in the form of contraction/dilation along a subset of the principal axes. This article derives some properties for this distribution, including its moment generating function, multivariate skewness, and kurtosis, and discusses its role as a population model for asymmetric principal components analysis. Maximum likelihood estimators and a complete Bayesian analysis, including inference on the number of skewed dimensions and their directions, are presented.
引用
收藏
页码：1123 / 1140
页数：18
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