Generic Identifiability of Linear Structural Equation Models by Ancestor Decomposition
Generic Identifiability of Linear Structural Equation Models by Ancestor Decomposition
复制标题
DOI：
10.1111/sjos.12227
复制
发表时间：
2016-12-01
影响因子：
1
通讯作者：
Weihs, Luca
中科院分区：
文献类型：
--
作者：
Drton, Mathias;Weihs, Luca
线性结构方程模型通过线性相关性和高斯噪声将随机变量联系起来，是建模多元联合分布的常用工具。这些模型对应于混合图，该混合图包括分别表示噪声项之间的线性关系和相关性的有向边和双向边。这些模型感兴趣的问题是参数可识别性，即是否可以从随机变量的联合协方差矩阵中恢复边缘系数。对于确定通用参数可识别性的问题，我们提出了一种基于半行程准则的算法。我们新算法的基础是图中顶点的祖先子集可用于扩展分解技术的适用性。
Linear structural equation models, which relate random variables via linear interdependencies and Gaussian noise, are a popular tool for modelling multivariate joint distributions. The models correspond to mixed graphs that include both directed and bidirected edges representing the linear relationships and correlations between noise terms, respectively. A question of interest for these models is that of parameter identifiability, whether or not it is possible to recover edge coefficients from the joint covariance matrix of the random variables. For the problem of determining generic parameter identifiability, we present an algorithm building upon the half-trek criterion. Underlying our new algorithm is the idea that ancestral subsets of vertices in the graph can be used to extend the applicability of a decomposition technique.