This paper introduces a method for studying the correlation structure of a range of responses modelled by a multivariate generalised linear mixed model (MGLMM). The methodology requires the existence of clusters of observations and that each of the several responses studied is modelled using a generalised linear mixed models (GLMM) containing random components representing the clusters. We construct a MGLMM by assuming that the distribution of each of the random components representing the clusters is the marginal distribution of a (sufficiently regular) multivariate elliptically contoured distribution. We use an undirected graphical model to represent the correlation structure of the random components representing the clusters of observations for each response. This representation allows us to draw conclusions regarding unknown underlying determining factors related to the clusters of observations. Using a combination of an undirected graph and a directed acyclic graph (DAG), we jointly represent the correlation structure of the responses and the related random components. Applying the theory of graphical models allows us to describe and draw conclusions on the correlation and, in some cases, the dependence between responses of different statistical nature (\eg following different distributions, different linear predictors and link functions). We present some simulation studies illustrating the proposed methodology.
翻译:本文介绍了一种方法,用于研究以多变通用线性混合模型(MGLMM)为模型的一系列答复的关联结构。该方法要求存在观测组群,所研究的几项答复中的每一项都是使用含有代表这些组群的随机组成部分的泛线性混合模型(GLMM)进行模拟的。我们构建了一个MGLMMM,假设代表这些组群的每个随机组成部分的分布是一个(足够经常的)多变形等式分布的边际分布。我们使用一个无方向的图形模型来代表代表代表每个反应组群观测的随机组成部分的关联结构。这种表示使我们能够就与观测组群有关的未知的基本决定因素作出结论。我们使用非定向图表和定向环形图(DAG)的组合,共同代表这些答复和相关随机组成部分的关联结构。应用图形模型理论,使我们得以描述不同统计性质答复的关联性,并在某些情况下对不同统计性质的答复之间的依赖性作出结论(根据不同分布、不同线性预测和联系功能进行)。我们提出一些模拟研究,说明拟议的方法。