Testing Multivariate Symmetry
Testing Multivariate Symmetry
复制标题
测试多元对称性
DOI：
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
B. Cheng
中科院分区：
文献类型：
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作者：
C. Heathcote;S. Rachev;B. Cheng
本文提出了一种检验一般多元分布关于一点对称性的方法，同时也提出了一种适用于多元稳定律特殊性质的检验方法。在一般情况下，使用的是从经验特征函数导出的随机过程。在对称条件下，建立了弱收敛于高斯过程的条件，并定义了一个检验统计量。与单变量情况下的情况不同，它被发现方便估计对称中心和球形修剪平均用于该目的。具体涉及多元稳定的法律程序是基于估计的谱措施和指数的稳定性。给出了一个关于二元分布的数值例子。
The paper presents a procedure for testing a general multivariate distribution for symmetry about a point and, also, a procedure adapted to the special properties of multivariate stable laws. In the general case use is made of a stochastic process derived from the empirical characteristic function. Under symmetry weak convergence to a Gaussian process is established and a test statistic is defined in terms of this limit process. Unlike circumstances in the univariate case, it is found convenient to estimate the center of symmetry and a spherically trimmed mean is used for that purpose. The procedure specifically concerned with multivariate stable laws is based on estimates of the spectral measure and index of stability. A numerical example concerning a bivariate distribution is given.