# Sure Screening for Gaussian Graphical Models

arXiv: https://arxiv.org/abs/1407.7819 · HTML: https://mlsanity.com/paper/1407.7819/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2014.

## Built on by (most cited first, 1 of 1)

- [Learning Massive-scale Partial Correlation Networks in Clinical Multi-omics Studies with HP-ACCORD](https://mlsanity.com/paper/2412.11554/) (2024)

## Abstract (arXiv)

We propose {graphical sure screening}, or GRASS, a very simple and computationally-efficient screening procedure for recovering the structure of a Gaussian graphical model in the high-dimensional setting. The GRASS estimate of the conditional dependence graph is obtained by thresholding the elements of the sample covariance matrix. The proposed approach possesses the sure screening property: with very high probability, the GRASS estimated edge set contains the true edge set. Furthermore, with high probability, the size of the estimated edge set is controlled. We provide a choice of threshold for GRASS that can control the expected false positive rate. We illustrate the performance of GRASS in a simulation study and on a gene expression data set, and show that in practice it performs quite competitively with more complex and computationally-demanding techniques for graph estimation.