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A Bayesian semiparametric Gaussian copula approach to a multivariate normality test

机译:贝叶斯半甲酰谱谱系多变量正常测试方法

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Semiparametric copulas are useful tools for modeling a multivariate distribution whose dependence structure is induced by a known copula and whose marginal distributions are estimated. In this paper, a Bayesian semiparametric copula approach is used to model the underlying multivariate distribution Ftrue. First, the Dirichlet process is constructed on the unknown marginal distributions of F-true. Then a Gaussian copula model is utilized to capture the dependence structure of F-true. As a result, a Bayesian multivariate normality test is developed by combining the relative belief ratio and the Energy distance. Various interesting theoretical results of the approach are derived. Several examples that cover the high dimensional case are discussed to illustrate the approach.
机译:Semiparametric Copulas是用于建模多变量分布的有用工具,其依赖性结构由已知的谱系引起并且估计其边际分布。在本文中,贝叶斯半甲酰胺拷贝方法用于模拟底层多变量分配FTRUE。首先,Dirichlet工艺构造在F-True的未知边际分布上。然后利用高斯Copula模型来捕获F-True的依赖结构。结果,通过组合相对信仰比和能量距离来开发贝叶斯多变量正常测试。衍生出各种有趣的方法的理论结果。讨论了覆盖高尺寸壳体的几个示例以说明该方法。

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