{"corpus_id":9759095,"paper_sha":"7c1f65ff771212d1942e5221852cda419a287e5e","doi":"10.1016/j.amc.2011.03.124","arxiv_id":"1006.0042","pmid":null,"pmcid":null,"mag_id":2963753598,"dblp_id":"journals/amc/PerkinsTW11","acl_id":null,"title":"Computing the confidence levels for a root-mean-square test of goodness-of-fit","year":2010,"publication_date":"2010-06-01","venue":"Applied Mathematics and Computation","journal":{"name":"Appl. Math. Comput.","pages":"9072-9084","volume":"217"},"journal_issn":null,"journal_title":null,"publication_types":["JournalArticle"],"pubmed_pub_types":null,"s2_fields_of_study":["Mathematics","Computer Science"],"reference_count":16,"citation_count":29,"influential_citation_count":1,"is_open_access":true,"arxiv_categories":["stat.CO","stat.ME"],"arxiv_license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","arxiv_journal_ref":"Applied Mathematics and Computation, 217 (22): 9072-9084, 2011","mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":"https://arxiv.org/pdf/1006.0042","s2_open_access_landing_url":"https://www.semanticscholar.org/paper/7c1f65ff771212d1942e5221852cda419a287e5e","s2_open_access_license":null,"s2_open_access_status":"GREEN","pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"The classic chi-squared statistic for testing goodness-of-fit has long been a cornerstone of modern statistical practice. The statistic consists of a sum in which each summand involves division by the probability associated with the corresponding bin in the distribution being tested for goodness-of-fit. Typically this division should precipitate rebinning to uniformize the probabilities associated with the bins, in order to make the test reasonably powerful. With the now widespread availability of computers, there is no longer any need for this. The present paper provides efficient black-box algorithms for calculating the asymptotic confidence levels of a variant on the classic chi-squared test which omits the problematic division. In many circumstances, it is also feasible to compute the exact confidence levels via Monte Carlo simulation.","claims":[{"public_id":"cl_e69b77f2d787a29cb7a0d2bc1568b470","status":"active","text":"Efficient black-box algorithms are provided for calculating asymptotic confidence levels of a root-mean-square variant of the classic chi-squared goodness-of-fit test that omits the division by bin probabilities.","confidence":0.97,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_e69b77f2d787a29cb7a0d2bc1568b470"},{"public_id":"cl_930940bbd0e22a72f4ba742c49348efa","status":"active","text":"Exact confidence levels are feasible to compute via Monte Carlo simulation in many circumstances.","confidence":0.93,"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous 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