COPULA GAUSSIAN GRAPHICAL MODELS AND THEIR APPLICATION TO MODELING FUNCTIONAL DISABILITY DATA
COPULA GAUSSIAN GRAPHICAL MODELS AND THEIR APPLICATION TO MODELING FUNCTIONAL DISABILITY DATA
复制标题
DOI：
10.1214/10-aoas397
复制
发表时间：
2011-06-01
影响因子：
1.8
通讯作者：
Lenkoski, Alex
中科院分区：
文献类型：
--
作者：
Dobra, Adrian;Lenkoski, Alex
我们在观察性研究中提出了一种综合的贝叶斯方法，以确定图形模型，该方法可以同时适应二进制，序数或连续变量。我们的新模型称为Copula Gaussian图形模型（CGGM）和嵌入式图形模型选择，内部是半参数Gaussian Copula中的。我们方法的适用性领域非常广泛，包括社会科学和经济学的许多研究。我们说明了Copula高斯图形模型在16维功能性残疾偶性表中的使用。
We propose a comprehensive Bayesian approach for graphical model determination in observational studies that can accommodate binary, ordinal or continuous variables simultaneously. Our new models are called copula Gaussian graphical models (CGGMs) and embed graphical model selection inside a semiparametric Gaussian copula. The domain of applicability of our methods is very broad and encompasses many studies from social science and economics. We illustrate the use of the copula Gaussian graphical models in the analysis of a 16-dimensional functional disability contingency table.