Papers › Structure estimation of binary graphical models on stratified data: application to the...
Structure estimation of binary graphical models on stratified data: application to the description of injury tables for victims of road accidents
Nadim Ballout, Vivian Viallon
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections