In this work we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data. We further show how to circumvent a priori assumptions on the marginals with semiparametric modelling. The joint distribution learned through this algorithm is considerably closer to the underlying distribution than existing methods.

翻译：在这项工作中,我们提出了严格应用期望最大化算法来确定边际分布和依赖性结构,在缺少数据的高山千叶模型中确定边际分布和依赖性结构。我们进一步展示了如何绕过对边际的先验假设,使用半参数模型。通过这一算法获得的联合分布比现有方法更接近于基本分布。