Regularized Gaussian Psychological Networks: Brief Report on the Performance of Extended BIC Model Selection

Simulation results indicate that GeLasso works well as an out-of-the-box method to estimate a psychological network structure.

Abstract

In recent literature, the Gaussian Graphical model (GGM; Lauritzen, 1996), a network of partial correlation coefficients, has been used to capture potential dynamic relationships between psychological variables. The GGM can be estimated using regularization in combination with model selection using the extended Bayesian Information Criterion (Foygel and Drton, 2010). I term this methodology GeLasso, and asses its performance using a plausible psychological network structure with both continuous and ordinal datasets. Simulation results indicate that GeLasso works well as an out-of-the-box method to estimate a psychological network structure.