Mixed Marginal Copula Modeling
扩展了连接函数在离散或连续边际上的应用，处理部分边际为离散与连续混合的情况，通过定义混合测度下的似然函数，采用贝叶斯推断和马尔可夫链蒙特卡洛估计，并应用于多元收入动态模型。
This article extends the literature on copulas with discrete or continuous marginals to the case where some of the marginals are a mixture of discrete and continuous components. We do so by carefully defining the likelihood as the density of the observations with respect to a mixed measure. The treatment is quite general, although we focus on mixtures of Gaussian and Archimedean copulas. The inference is Bayesian with the estimation carried out by Markov chain Monte Carlo. We illustrate the methodology and algorithms by applying them to estimate a multivariate income dynamics model. Supplementary materials for this article are available online.