论文标题
用缺少数据估算高斯库氏
Estimating Gaussian Copulas with Missing Data
论文作者
论文摘要
在这项工作中，我们介绍了期望最大化算法的严格应用，以确定高斯副群模型中缺少数据的边际分布和依赖性结构。我们进一步展示了如何通过半摩托车建模对边际上的先验假设进行规避。通过该算法学到的联合分布比现有方法更接近基础分布。
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.
