A Stepwise Approach for High-Dimensional Gaussian Graphical Models Articles uri icon

publication date

  • January 2021

issue

  • 2

volume

  • 1

International Standard Serial Number (ISSN)

  • 2773-0689

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.

subjects

  • Statistics

keywords

  • covariance selection; gaussian graphical model; forward and backward selection; partial correlation coefficient