Global identifiability of linear structural equation models
Abstract
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.
Cite
@article{arxiv.1003.1146,
title = {Global identifiability of linear structural equation models},
author = {Mathias Drton and Rina Foygel and Seth Sullivant},
journal= {arXiv preprint arXiv:1003.1146},
year = {2011}
}
Comments
Published in at http://dx.doi.org/10.1214/10-AOS859 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)