{"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/semi-analytic-resampling-in-lasso","title":"Semi-Analytic Resampling in Lasso","arxiv_id":"1802.10254","date":"2018-02-28","proceeding":null,"authors":["Tomoyuki Obuchi","Yoshiyuki Kabashima"],"abstract":"An approximate method for conducting resampling in Lasso, the $\\ell_1$\npenalized linear regression, in a semi-analytic manner is developed, whereby\nthe average over the resampled datasets is directly computed without repeated\nnumerical sampling, thus enabling an inference free of the statistical\nfluctuations due to sampling finiteness, as well as a significant reduction of\ncomputational time. The proposed method is based on a message passing type\nalgorithm, and its fast convergence is guaranteed by the state evolution\nanalysis, when covariates are provided as zero-mean independently and\nidentically distributed Gaussian random variables. It is employed to implement\nbootstrapped Lasso (Bolasso) and stability selection, both of which are\nvariable selection methods using resampling in conjunction with Lasso, and\nresolves their disadvantage regarding computational cost. To examine\napproximation accuracy and efficiency, numerical experiments were carried out\nusing simulated datasets. Moreover, an application to a real-world dataset, the\nwine quality dataset, is presented. To process such real-world datasets, an\nobjective criterion for determining the relevance of selected variables is also\nintroduced by the addition of noise variables and resampling.","url_abs":"http://arxiv.org/abs/1802.10254v2","url_pdf":"http://arxiv.org/pdf/1802.10254v2.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":"semi-analytic-resampling-in-lasso","repo_url":"https://github.com/T-Obuchi/AMPR_lasso_matlab","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"variable-selection","task_name":"Variable Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}