{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T13:36:57Z","timestamp":1773668217285,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T00:00:00Z","timestamp":1718323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>In this study, we introduce the modified Burr III Odds Ratio\u2013G distribution, a novel statistical model that integrates the odds ratio concept with the foundational Burr III distribution. The spotlight of our investigation is cast on a key subclass within this innovative framework, designated as the Burr III Scaled Inverse Odds Ratio\u2013G (B-SIOR-G) distribution. By effectively integrating the odds ratio with the Burr III distribution, this model enhances both flexibility and predictive accuracy. We delve into a thorough exploration of this distribution family\u2019s mathematical and statistical properties, spanning hazard rate functions, quantile functions, moments, and additional features. Through rigorous simulation, we affirm the robustness of the B-SIOR-G model. The flexibility and practicality of the B-SIOR-G model are demonstrated through its application to four datasets, highlighting its enhanced efficacy over several well-established distributions.<\/jats:p>","DOI":"10.3390\/axioms13060401","type":"journal-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T10:42:34Z","timestamp":1718361754000},"page":"401","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Enhanced Real-Life Data Modeling with the Modified Burr III Odds Ratio\u2013G Distribution"],"prefix":"10.3390","volume":"13","author":[{"given":"Haochong","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Statistical Sciences, University of Toronto, Toronto, ON M5G 1Z5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5643-5685","authenticated-orcid":false,"given":"Mingfang","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University of West Florida, Pensacola, FL 32514, USA"}]},{"given":"Xinyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University of West Florida, Pensacola, FL 32514, USA"}]},{"given":"Ziyan","family":"He","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Wisconsin-Madison, Madison, WI 53706, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0148-1734","authenticated-orcid":false,"given":"Shusen","family":"Pu","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University of West Florida, Pensacola, FL 32514, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1002\/(SICI)1096-9934(199608)16:5<545::AID-FUT3>3.0.CO;2-G","article-title":"Recovering probabilistic information from option markets: Tests of distributional assumptions","volume":"16","author":"Sherrick","year":"1996","journal-title":"J. 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