GLOBAL IDENTIFIABILITY OF LINEAR STRUCTURAL EQUATION MODELS

• 成果类型: Article
• 署名作者: Drton, Mathias; Foygel, Rina; Sullivant, Seth
• 署名单位: University of Chicago; North Carolina State University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/10-AOS859
• 发表日期: 2011
• 页码: 865-886
• 关键词: correlated errors

摘要

Structural equation models are multivariate statistical models that are defined by specifying noisy functional relationships among random variables. We consider the classical case of linear relationships and additive Gaussian noise terms. We give a necessary and sufficient condition for global identifiability of the model in terms of a mixed graph encoding the linear structural equations and the correlation structure of the error terms. Global identifiability is understood to mean injectivity of the parametrization of the model and is fundamental in particular for applicability of standard statistical methodology.