{"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-hierarchical-generalized-linear-model-and","title":"The hierarchical generalized linear model and the bootstrap estimator of the error of prediction of loss reserves in a non-life insurance company","arxiv_id":"1612.04126","date":"2016-12-13","proceeding":null,"authors":[],"abstract":"This paper presents the hierarchical generalized linear model (HGLM) for loss\nreserving in a non-life insurance company. Because in this case the error of\nprediction is expressed by a complex analytical formula, the error bootstrap\nestimator is proposed instead. Moreover, the bootstrap procedure is used to\nobtain full information about the error by applying quantiles of the absolute\nprediction error. The full R code is available on the Github\nhttps://github.com/woali/BootErrorLossReserveHGLM.","url_abs":"http://arxiv.org/abs/1612.04126v1","url_pdf":"http://arxiv.org/pdf/1612.04126v1.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-hierarchical-generalized-linear-model-and","repo_url":"https://github.com/woali/BootErrorLossReserveHGLM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}