Kertel Maximilian, Pauly Markus
BMW Group, Battery Cell Competence Centre, 80788 Munich, Germany.
Department of Statistics, TU Dortmund University, 44227 Dortmund, Germany.
Entropy (Basel). 2022 Dec 19;24(12):1849. doi: 10.3390/e24121849.
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 modeling. Further, we outline how expert knowledge on the marginals and the dependency structure can be included. A simulation study shows that the distribution learned through this algorithm is closer to the true distribution than that obtained with existing methods and that the incorporation of domain knowledge provides benefits.
在这项工作中，我们提出了期望最大化算法的严格应用，以确定具有缺失数据的高斯Copula模型中的边际分布和相依结构。我们进一步展示了如何通过半参数建模规避对边际分布的先验假设。此外，我们概述了如何纳入关于边际分布和相依结构的专家知识。一项模拟研究表明，通过该算法学习到的分布比现有方法得到的分布更接近真实分布，并且纳入领域知识具有益处。
