{"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/nonparametric-bayesian-lomax-delegate-racing","title":"Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks","arxiv_id":"1810.08564","date":"2018-10-19","proceeding":"NeurIPS 2018 12","authors":["Quan Zhang","Mingyuan Zhou"],"abstract":"We propose Lomax delegate racing (LDR) to explicitly model the mechanism of\nsurvival under competing risks and to interpret how the covariates accelerate\nor decelerate the time to event. LDR explains non-monotonic covariate effects\nby racing a potentially infinite number of sub-risks, and consequently relaxes\nthe ubiquitous proportional-hazards assumption which may be too restrictive.\nMoreover, LDR is naturally able to model not only censoring, but also missing\nevent times or event types. For inference, we develop a Gibbs sampler under\ndata augmentation for moderately sized data, along with a stochastic gradient\ndescent maximum a posteriori inference algorithm for big data applications.\nIllustrative experiments are provided on both synthetic and real datasets, and\ncomparison with various benchmark algorithms for survival analysis with\ncompeting risks demonstrates distinguished performance of LDR.","url_abs":"http://arxiv.org/abs/1810.08564v2","url_pdf":"http://arxiv.org/pdf/1810.08564v2.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":"nonparametric-bayesian-lomax-delegate-racing","repo_url":"https://github.com/zhangquan-ut/Lomax-delegate-racing-for-survival-analysis-with-competing-risks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"survival-analysis","task_name":"Survival Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}