The correlation space of Gaussian latent tree models and model selection without fitting
The correlation space of Gaussian latent tree models and model selection without fitting
复制标题
高斯潜树模型的相关空间及无拟合模型选择
DOI：
10.1093/biomet/asw032
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发表时间：
2015
期刊：
影响因子：
2.7
通讯作者：
Jim Q. Smith
中科院分区：
文献类型：
--
作者：
Nathaniel Shiers;Piotr Zwiernik;J. Aston;Jim Q. Smith
我们提供了一个完整的描述可能的分布与任何高斯潜在树模型一致。这种描述包括多项式方程和不等式，涉及观测变量之间的协方差。测试不等式约束可以使用逆Wishart分布来完成，这导致树兼容性的简单初步评估。为了测试等式约束，我们采用四分体分析的一般技术。这种方法即使对于小样本也是有效的，并且可以很容易地调整以测试整个模型或树的特定宏观结构。我们的方法是简单的实现，不需要拟合的模型。通过分别在语言和生物环境中进行探索性和验证性四分体分析来说明该技术的多功能性。
We provide a complete description of possible distributions consistent with any Gaussian latent tree model. This description consists of polynomial equations and inequalities involving covariances between the observed variables. Testing inequality constraints can be done using the inverse Wishart distribution and this leads to simple preliminary assessment of tree-compatibility. To test equality constraints we employ general techniques of tetrad analyses. This approach is effective even for small sample sizes and can be easily adjusted to test either entire models or just particular macrostructures of a tree. Our methods are simple to implement and do not require fitting of the model. The versatility of the techniques is illustrated by performing exploratory and confirmatory tetrad analyses in linguistic and biological settings respectively.
影响因子：
1.6
作者：
Shiers N
通讯作者：
Shiers N