Structure estimation for mixed graphical models in high-dimensional data

TL;DR

This paper introduces a new method for estimating undirected graphical models involving variables of different types in high-dimensional data, filling a significant methodological gap.

Contribution

The authors develop a novel class of mixed graphical models combined with a structure estimation approach using generalized covariance matrices.

Findings

Method performs well in simulations

Successfully applied to Autism Spectrum Disorder data

Implementation available as an R-package

Abstract

Undirected graphical models are a key component in the analysis of complex observational data in a large variety of disciplines. In many of these applications one is interested in estimating the undirected graphical model underlying a distribution over variables with different domains. Despite the pervasive need for such an estimation method, to date there is no such method that models all variables on their proper domain. We close this methodological gap by combining a new class of mixed graphical models with a structure estimation approach based on generalized covariance matrices. We report the performance of our methods using simulations, illustrate the method with a dataset on Autism Spectrum Disorder (ASD) and provide an implementation as an R-package.