Model Selection With Graphical Neighbour Information

Accurate m odel selection is a fundamental requirement for statistical analysis (1 - 5) . In many real - world applications of graphical modelling, correct model structure ident ifica tion is the ultimate objective. S tandard model validation procedures such as information theoretic scores and cross validation have demonstr ated poor performance when . Specialised methods such as EBIC, StARS and RIC have been developed for the explicit purpose of high - dimensional Gaussian graphical model selection. We present a novel model score criterion, Graphical Neighbour Information. This method demonstrates oracle performance in high - dimensional model selection, outperforming the current state - of - the - a rt in our simulations. The Graphical Neighbour Information criterion has the additional advantage of efficient, closed - form computability, sparing the costly inference of multiple models on data subsamples. We provide a theoretic analysis of the method and benchmark simulations versus the current state of the art .