{"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/the-false-positive-control-lasso","title":"The False Positive Control Lasso","arxiv_id":"1903.12584","date":"2019-03-29","proceeding":null,"authors":["Erik Drysdale","Yingwei Peng","Timothy P. Hanna","Paul Nguyen","Anna Goldenberg"],"abstract":"In high dimensional settings where a small number of regressors are expected\nto be important, the Lasso estimator can be used to obtain a sparse solution\nvector with the expectation that most of the non-zero coefficients are\nassociated with true signals. While several approaches have been developed to\ncontrol the inclusion of false predictors with the Lasso, these approaches are\nlimited by relying on asymptotic theory, having to empirically estimate terms\nbased on theoretical quantities, assuming a continuous response class with\nGaussian noise and design matrices, or high computation costs. In this paper we\nshow how: (1) an existing model (the SQRT-Lasso) can be recast as a method of\ncontrolling the number of expected false positives, (2) how a similar estimator\ncan used for all other generalized linear model classes, and (3) this approach\ncan be fit with existing fast Lasso optimization solvers. Our justification for\nfalse positive control using randomly weighted self-normalized sum theory is to\nour knowledge novel. Moreover, our estimator's properties hold in finite\nsamples up to some approximation error which we find in practical settings to\nbe negligible under a strict mutual incoherence condition.","url_abs":"http://arxiv.org/abs/1903.12584v1","url_pdf":"http://arxiv.org/pdf/1903.12584v1.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":"the-false-positive-control-lasso","repo_url":"https://github.com/ErikinBC/fpclasso","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"the-false-positive-control-lasso","repo_url":"https://github.com/erikinbc/survset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"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}