Abstract Hierarchical marginal models have been proposed for categorical data to overcome some limitations of the log-linear approach in modeling marginal distributions. These models can easily satisfy marginal conditional independence conditions and describe with great flexibility the dependence of marginal distributions on covariates. As the richness of the family of hierarchical marginal models leads to comparing models that do not satisfy a nesting relationship, statistical tests for model selection from non-nested, possibly misspecified marginal models are introduced.

中文翻译：

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可能错误指定的分层多项式边际模型的选择测试

摘要针对分类数据提出了层次化的边际模型，以克服对数线性方法建模边际分布的某些限制。这些模型可以轻松满足边际条件独立性条件，并且可以灵活地描述边际分布对协变量的依赖性。由于层次化边际模型族的丰富性导致比较不满足嵌套关系的模型，因此引入了从非嵌套，可能指定不正确的边际模型中进行模型选择的统计检验。