{"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/high-dimensional-regression-over-disease","title":"High-dimensional regression over disease subgroups","arxiv_id":"1611.00953","date":"2016-11-03","proceeding":null,"authors":["Frank Dondelinger","Sach Mukherjee","the Alzheimer's Disease Neuroimaging Initiative"],"abstract":"We consider high-dimensional regression over subgroups of observations. Our\nwork is motivated by biomedical problems, where disease subtypes, for example,\nmay differ with respect to underlying regression models, but sample sizes at\nthe subgroup-level may be limited. We focus on the case in which\nsubgroup-specific models may be expected to be similar but not necessarily\nidentical. Our approach is to treat subgroups as related problem instances and\njointly estimate subgroup-specific regression coefficients. This is done in a\npenalized framework, combining an $\\ell_1$ term with an additional term that\npenalizes differences between subgroup-specific coefficients. This gives\nsolutions that are globally sparse but that allow information-sharing between\nthe subgroups. We present algorithms for estimation and empirical results on\nsimulated data and using Alzheimer's disease, amyotrophic lateral sclerosis and\ncancer datasets. These examples demonstrate the gains our approach can offer in\nterms of prediction and the ability to estimate subgroup-specific sparsity\npatterns.","url_abs":"http://arxiv.org/abs/1611.00953v2","url_pdf":"http://arxiv.org/pdf/1611.00953v2.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":"high-dimensional-regression-over-disease","repo_url":"https://github.com/FrankD/fuser","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}