论文标题
多元混合离散连续模型中的副群体转换
A copula transformation in multivariate mixed discrete-continuous models
论文作者
论文摘要
Copulas允许对复杂的依赖结构以及各种边际分布进行灵活且同时建模。尤其是如果密度函数可以表示为边际密度函数和copula密度函数的乘积，这既可以导致对条件分布的直观解释和方便的估计程序。但是，对于具有混合离散和连续边缘分布的Copula模型不再是这种情况，因为相应的密度函数不能很好地分解。在本文中，我们引入了一种副群体转换方法，该方法允许用混合离散和连续边缘的分布的密度函数作为边缘概率质量/密度函数和copula密度函数的乘积。使用提出的方法，可以通过分析描述条件分布，并且可以根据所使用的copula类型来降低估计过程中的计算复杂性。
Copulas allow a flexible and simultaneous modeling of complicated dependence structures together with various marginal distributions. Especially if the density function can be represented as the product of the marginal density functions and the copula density function, this leads to both an intuitive interpretation of the conditional distribution and convenient estimation procedures. However, this is no longer the case for copula models with mixed discrete and continuous marginal distributions, because the corresponding density function cannot be decomposed so nicely. In this paper, we introduce a copula transformation method that allows to represent the density function of a distribution with mixed discrete and continuous marginals as the product of the marginal probability mass/density functions and the copula density function. With the proposed method, conditional distributions can be described analytically and the computational complexity in the estimation procedure can be reduced depending on the type of copula used.
