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A copula transformation in multivariate mixed discrete-continuous models

机译：多变量混合离散 - 连续模型中的Copula转化

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摘要

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.(c) 2020 Elsevier B.V. All rights reserved.
机译：Copulas允许灵活，同时建模复杂依赖结构以及各种边际分布。特别是如果密度函数可以表示为边际密度函数和谱密度函数的乘积，这导致了对条件分布和方便估计程序的直观解释。然而，对于具有混合离散和连续边缘分布的Copula型号不再是套件，因为相应的密度函数不能如此恰当地分解。在本文中，我们介绍了一种概述了一种允许用混合离散和连续边缘的分布的密度函数作为边际概率质量/密度函数和谱密度函数的乘积。利用所提出的方法，可以在分析上描述条件分布，并且可以根据所用的谱的类型来减少估计过程中的计算复杂性。（c）2020 Elsevier B.v.保留所有权利。

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