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摘要翻译：
对数线性模型是列联表分析的经典工具。特别是，图形对数线性模型的子类为建模条件独立性提供了一个通用框架。然而，除了特殊的结构，边缘独立性假设不能被这些传统的模型所容纳。针对二元变量，我们提出了一个模型类，它为列联表中的边际独立性建模提供了一个框架。所采用的方法是图形化的，并借鉴了多元高斯模型的边缘独立性。对于图形模型表示，我们使用双向图，这是路径图的传统。我们展示了如何以一种简单的方式将模型参数化，以及如何使用迭代条件拟合算法的版本来执行最大似然估计。最后，我们考虑将这些模型与对称性约束相结合。
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英文标题：
《Binary Models for Marginal Independence》
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作者：
Mathias Drton, Thomas S. Richardson
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最新提交年份：
2007
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分类信息：
一级分类：Mathematics 数学
二级分类：Statistics Theory 统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics 统计学
二级分类：Statistics Theory 统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
Log-linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log-linear models provides a general framework for modelling conditional independences. However, with the exception of special structures, marginal independence hypotheses cannot be accommodated by these traditional models. Focusing on binary variables, we present a model class that provides a framework for modelling marginal independences in contingency tables. The approach taken is graphical and draws on analogies to multivariate Gaussian models for marginal independence. For the graphical model representation we use bi-directed graphs, which are in the tradition of path diagrams. We show how the models can be parameterized in a simple fashion, and how maximum likelihood estimation can be performed using a version of the Iterated Conditional Fitting algorithm. Finally we consider combining these models with symmetry restrictions.
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PDF链接：
https://arxiv.org/pdf/707.3794
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