{"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/rcr-robust-compound-regression-for-robust","title":"RCR: Robust Compound Regression for Robust Estimation of Errors-in-Variables Model","arxiv_id":"1508.02925","date":"2015-08-12","proceeding":null,"authors":["Hao Han","Wei Zhu"],"abstract":"The errors-in-variables (EIV) regression model, being more realistic by\naccounting for measurement errors in both the dependent and the independent\nvariables, is widely adopted in applied sciences. The traditional EIV model\nestimators, however, can be highly biased by outliers and other departures from\nthe underlying assumptions. In this paper, we develop a novel nonparametric\nregression approach - the robust compound regression (RCR) analysis method for\nthe robust estimation of EIV models. We first introduce a robust and efficient\nestimator called least sine squares (LSS). Taking full advantage of both the\nnew LSS method and the compound regression analysis method developed in our own\ngroup, we subsequently propose the RCR approach as a generalization of those\ntwo, which provides a robust counterpart of the entire class of the maximum\nlikelihood estimation (MLE) solutions of the EIV model, in a 1-1 mapping.\nTechnically, our approach gives users the flexibility to select from a class of\nRCR estimates the optimal one with a predefined regression efficiency criterion\nsatisfied. Simulation studies and real-life examples are provided to illustrate\nthe effectiveness of the RCR approach.","url_abs":"http://arxiv.org/abs/1508.02925v1","url_pdf":"http://arxiv.org/pdf/1508.02925v1.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":"rcr-robust-compound-regression-for-robust","repo_url":"https://github.com/yangyucheng000/Paper-3/tree/main/RCR_MindSpore","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"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}