{"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/censored-quantile-regression-forests","title":"Censored Quantile Regression Forests","arxiv_id":"1902.03327","date":"2019-02-08","proceeding":null,"authors":["Alexander Hanbo Li","Jelena Bradic"],"abstract":"Random forests are powerful non-parametric regression method but are severely\nlimited in their usage in the presence of randomly censored observations, and\nnaively applied can exhibit poor predictive performance due to the incurred\nbiases. Based on a local adaptive representation of random forests, we develop\nits regression adjustment for randomly censored regression quantile models.\nRegression adjustment is based on new estimating equations that adapt to\ncensoring and lead to quantile score whenever the data do not exhibit\ncensoring. The proposed procedure named censored quantile regression forest,\nallows us to estimate quantiles of time-to-event without any parametric\nmodeling assumption. We establish its consistency under mild model\nspecifications. Numerical studies showcase a clear advantage of the proposed\nprocedure.","url_abs":"http://arxiv.org/abs/1902.03327v1","url_pdf":"http://arxiv.org/pdf/1902.03327v1.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":"censored-quantile-regression-forests","repo_url":"https://github.com/AlexanderYogurt/censored_ExtremelyRandomForest","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"quantile-regression","task_name":"quantile regression"},{"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}