Structure estimation of binary graphical models on stratified data: application to the description of injury tables for victims of road accidents

TL;DR

This paper introduces new methods for estimating binary graphical models across stratified data, improving accuracy in applications like injury analysis in road accident victims.

Contribution

It combines existing estimation techniques with structured sparsity penalties to better handle stratified data, demonstrating superior performance over competitors.

Findings

Methods outperform competitors on synthetic data

Application reveals injury associations by road user type

Structured sparsity improves model estimation accuracy

Abstract

Graphical models are used in many applications such as medical diagnostic, computer security, etc. More and more often, the estimation of such models has to be performed on several predefined strata of the whole population. For instance, in epidemiology and clinical research, strata are often defined according to age, gender, treatment or disease type, etc. In this article, we propose new approaches aimed at estimating binary graphical models on such strata. Our approaches are obtained by combining well-known methods when estimating one single binary graphical model, with penalties encouraging structured sparsity, and which have recently been shown appropriate when dealing with stratified data. Empirical comparions on synthetic data highlight that our approaches generally outperform the competitors we considered. An application is provided where we study associations among injuries suffered by victims of road accidents according to road user type.