{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sure-screening-for-gaussian-graphical-models","title":"Sure Screening for Gaussian Graphical Models","arxiv_id":"1407.7819","date":"2014-07-29","proceeding":null,"authors":["Shikai Luo","Rui Song","Daniela Witten"],"abstract":"We propose {graphical sure screening}, or GRASS, a very simple and\ncomputationally-efficient screening procedure for recovering the structure of a\nGaussian graphical model in the high-dimensional setting. The GRASS estimate of\nthe conditional dependence graph is obtained by thresholding the elements of\nthe sample covariance matrix. The proposed approach possesses the sure\nscreening property: with very high probability, the GRASS estimated edge set\ncontains the true edge set. Furthermore, with high probability, the size of the\nestimated edge set is controlled. We provide a choice of threshold for GRASS\nthat can control the expected false positive rate. We illustrate the\nperformance of GRASS in a simulation study and on a gene expression data set,\nand show that in practice it performs quite competitively with more complex and\ncomputationally-demanding techniques for graph estimation.","url_abs":"http://arxiv.org/abs/1407.7819v1","url_pdf":"http://arxiv.org/pdf/1407.7819v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sure-screening-for-gaussian-graphical-models","repo_url":"https://github.com/Mamba413/gif","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"sure-screening-for-gaussian-graphical-models","repo_url":"https://github.com/cran/gif","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}