{"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/robust-estimation-of-self-exciting","title":"Robust Estimation of Self-Exciting Generalized Linear Models with Application to Neuronal Modeling","arxiv_id":"1507.03955","date":"2015-07-14","proceeding":null,"authors":["Abbas Kazemipour","Min Wu","Behtash Babadi"],"abstract":"We consider the problem of estimating self-exciting generalized linear models\nfrom limited binary observations, where the history of the process serves as\nthe covariate. We analyze the performance of two classes of estimators, namely\nthe $\\ell_1$-regularized maximum likelihood and greedy estimators, for a\ncanonical self-exciting process and characterize the sampling tradeoffs\nrequired for stable recovery in the non-asymptotic regime. Our results extend\nthose of compressed sensing for linear and generalized linear models with\ni.i.d. covariates to those with highly inter-dependent covariates. We further\nprovide simulation studies as well as application to real spiking data from the\nmouse's lateral geniculate nucleus and the ferret's retinal ganglion cells\nwhich agree with our theoretical predictions.","url_abs":"http://arxiv.org/abs/1507.03955v3","url_pdf":"http://arxiv.org/pdf/1507.03955v3.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":"robust-estimation-of-self-exciting","repo_url":"https://github.com/kaazemi/PPSelf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"compressed-sensing","task_name":"compressed sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}