A Stepwise Approach for High-Dimensional Gaussian Graphical Models

Abstract

We present a stepwise approach to estimate high dimensional Gaussian graphical
models. We exploit the relation between the partial correlation coefficients
and the distribution of the prediction errors, and parametrize the model in terms
of the Pearson correlation coefficients between the prediction errors of the nodes’
best linear predictors. We propose a novel stepwise algorithm for detecting pairs
of conditionally dependent variables. We compare the proposed algorithm with
existing methods including graphical lasso (Glasso), constrained `l1-minimization
(CLIME) and equivalent partial correlation (EPC), via simulation studies and
real life applications. In our simulation study we consider several model settings
and report the results using different performance measures that look at desirable
features of the recovered graph.