文章基本信息

标题：Identifiability of directed Gaussian graphical models with one latent source
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作者：Dennis Leung ; Mathias Drton ; Hisayuki Hara 等
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2016
卷号：10
期号：1
页码：394-422
DOI：10.1214/16-EJS1111
语种：English
出版社：Institute of Mathematical Statistics
摘要：We study parameter identifiability of directed Gaussian graphical models with one latent variable. In the scenario we consider, the latent variable is a confounder that forms a source node of the graph and is a parent to all other nodes, which correspond to the observed variables. We give a graphical condition that is sufficient for the Jacobian matrix of the parametrization map to be full rank, which entails that the parametrization is generically finite-to-one, a fact that is sometimes also referred to as local identifiability. We also derive a graphical condition that is necessary for such identifiability. Finally, we give a condition under which generic parameter identifiability can be determined from identifiability of a model associated with a subgraph. The power of these criteria is assessed via an exhaustive algebraic computational study for small models with 4, 5, and 6 observable variables, and a simulation study for large models with 25 or 35 observable variables.