Estimation of means in graphical Gaussian models with symmetries

Abstract

We study the problem of estimability of means in undirected graphical Gaussian models with symmetry restrictions represented by a colored graph. Following on from previous studies, we partition the variables into sets of vertices whose corresponding means are restricted to being identical. We find a necessary and sufficient condition on the partition to ensure equality between the maximum likelihood and least-squares estimators of the mean.

Published in at http://dx.doi.org/10.1214/12-AOS991 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (1)

• Lattices of Graphical Gaussian Models with Symmetries

Cited by in corpus (4)

• Geometry of maximum likelihood estimation in Gaussian graphical models
• G-AMA: Sparse Gaussian graphical model estimation via alternating minimization
• Symmetrically colored Gaussian graphical models with toric vanishing ideal
• Approximate Bayesian estimation in large coloured graphical Gaussian models