Brief Report on Estimating Regularized Gaussian Networks from Continuous and Ordinal Data

TL;DR

This paper evaluates GeLasso, a regularized Gaussian Graphical Model estimation method, demonstrating its effectiveness in accurately recovering network structures from both continuous and ordinal data in psychological research.

Contribution

The paper introduces and assesses GeLasso, a novel approach for estimating Gaussian networks from mixed data types, showing its practical utility and robustness.

Findings

GeLasso accurately estimates network structures in simulations.

It performs well with both continuous and ordinal data.

The method is effective as an out-of-the-box solution.

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 observed 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 network structures.