Killiches Matthias, Czado Claudia
Zentrum Mathematik, Technische Universität München, Boltzmannstraße 3, 85748 Garching, Germany.
Biometrics. 2018 Sep;74(3):997-1005. doi: 10.1111/biom.12867. Epub 2018 Mar 22.
We propose a model for unbalanced longitudinal data, where the univariate margins can be selected arbitrarily and the dependence structure is described with the help of a D-vine copula. We show that our approach is an extremely flexible extension of the widely used linear mixed model if the correlation is homogeneous over the considered individuals. As an alternative to joint maximum-likelihood a sequential estimation approach for the D-vine copula is provided and validated in a simulation study. The model can handle missing values without being forced to discard data. Since conditional distributions are known analytically, we easily make predictions for future events. For model selection, we adjust the Bayesian information criterion to our situation. In an application to heart surgery data our model performs clearly better than competing linear mixed models.
我们提出了一种用于不平衡纵向数据的模型，其中单变量边缘可以任意选择，并且借助D - 藤Copula描述依赖结构。我们表明，如果在所考虑的个体中相关性是齐次的，那么我们的方法是广泛使用的线性混合模型的一种极其灵活的扩展。作为联合最大似然法的替代方法，我们提供了一种用于D - 藤Copula的序贯估计方法，并在模拟研究中进行了验证。该模型可以处理缺失值，而不必被迫丢弃数据。由于条件分布可以通过解析方法得到，我们可以轻松地对未来事件进行预测。对于模型选择，我们根据我们的情况调整了贝叶斯信息准则。在应用于心脏手术数据时，我们的模型表现明显优于竞争的线性混合模型。
