{"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/a-new-class-of-binning-free-multivariate","title":"A new class of binning free, multivariate goodness-of-fit tests: the energy tests","arxiv_id":"hep-ex/0203010","date":"2002-03-07","proceeding":null,"authors":["B. Aslan","G. Zech"],"abstract":"We present a new class of multivariate binning-free and nonparametric goodness-of-fit tests. The test quantity \\emph{energy} is a function of the distances of observed and simulated observations in the variate space. The simulation follows the probability distribution function $f_{0}$ of the null hypothesis. The distances are weighted with a weighting function which can be adjusted to the variations of $f_{0}$. We have investigated the power of the test for a uniform and a Gaussian distribution of one or two variates, respectively and compared it to that of conventional tests. The energy test with a Gaussian weighting function is closely related to the Pearson $\\chi ^{2}$ test but is more powerful in most applications and avoids arbitrary bin boundaries. The test is especially powerful in the multivariate case.","url_abs":"https://arxiv.org/abs/hep-ex/0203010v5","url_pdf":"https://arxiv.org/pdf/hep-ex/0203010v5.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-new-class-of-binning-free-multivariate","repo_url":"https://github.com/ftorresd/HZToUpsilonPhotonSandbox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"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}