{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T23:51:49Z","timestamp":1778802709673,"version":"3.51.4"},"reference-count":18,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Imam Mohammad Ibn Saud Islamic University","award":["IMSIU-DDRSP2502"],"award-info":[{"award-number":["IMSIU-DDRSP2502"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In this study, we introduced and analyzed the Slash\u2013Log\u2013Logistic (SlaLL) distribution, a novel statistical model developed by applying the slash methodology to log\u2013logistic and beta distributions. The SlaLL distribution is particularly suited for modeling datasets characterized by heavy tails and extreme values, frequently encountered in survival time analyses. We derived the mathematical representation of the distribution involving Gauss hypergeometric and beta functions, explicitly established the probability density function, cumulative distribution function, hazard rate function, and reliability function, and provided clear definitions of its moments. Through comprehensive simulation studies, the accuracy and robustness of maximum likelihood and Bayesian methods for parameter estimation were validated. Comparative empirical analyses demonstrated the SlaLL distribution\u2019s superior fitting performance over well-known slash-based models, emphasizing its practical utility in accurately capturing the complexities of real-world survival time data.<\/jats:p>","DOI":"10.3390\/sym17111795","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T05:34:38Z","timestamp":1761284078000},"page":"1795","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Gauss Hypergeometric-Type Model for Heavy-Tailed Survival Times in Biomedical Research"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1329-8283","authenticated-orcid":false,"given":"Jiju","family":"Gillariose","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science, Christ University, Hosur Road, Bangalore 560029, Karnataka, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5758-1035","authenticated-orcid":false,"given":"Mahmoud M.","family":"Abdelwahab","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8304-0005","authenticated-orcid":false,"given":"Joshin","family":"Joseph","sequence":"additional","affiliation":[{"name":"School of Commerce and Professional Studies, Marian College Kuttikkanam, Kuttikkanam P.O., Peermade, Idukki 685531, Kerala, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7680-6762","authenticated-orcid":false,"given":"Mustafa M.","family":"Hasaballah","sequence":"additional","affiliation":[{"name":"Department of Basic Sciences, Marg Higher Institute of Engineering and Modern Technology, Cairo 11721, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.2307\/1909287","article-title":"The Graduation of Income Distributions","volume":"29","author":"Fisk","year":"1961","journal-title":"Econometrica"},{"key":"ref_2","first-page":"165","article-title":"Log\u2013logistic regression models for survival data","volume":"32","author":"Bennett","year":"1983","journal-title":"J. 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