# Robust and sparse Gaussian graphical modeling under cell-wise contamination

arXiv: https://arxiv.org/abs/1802.05475 · HTML: https://mlsanity.com/paper/1802.05475/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2018.

## Built on by (most cited first, 3 of 3)

- [The Cellwise Minimum Covariance Determinant Estimator](https://mlsanity.com/paper/2207.13493/) (2022)
- [Cellwise Outliers](https://mlsanity.com/paper/2604.14182/) (2026)
- [Log-Ratio Propagation on the Simplex: A Theory of Cellwise Contamination for Compositional Data](https://mlsanity.com/paper/2605.31345/) (2026)

## Abstract (arXiv)

Graphical modeling explores dependences among a collection of variables by inferring a graph that encodes pairwise conditional independences. For jointly Gaussian variables, this translates into detecting the support of the precision matrix. Many modern applications feature high-dimensional and contaminated data that complicate this task. In particular, traditional robust methods that down-weight entire observation vectors are often inappropriate as high-dimensional data may feature partial contamination in many observations. We tackle this problem by giving a robust method for sparse precision matrix estimation based on the γ-divergence under a cell-wise contamination model. Simulation studies demonstrate that our procedure outperforms existing methods especially for highly contaminated data.