https://iphopen.org/index.php/As <p><strong>IPHO-Journal of Advance Research in Applied Science.<a href="https://portal.issn.org/resource/ISSN/3050-8835">(e-ISSN 3050-8835, p-ISSN 3050-9289)</a></strong> Publishes a wide range of high quality research articles in the field (but not limited to) given below: Biology, Physics, Chemistry, Pharmacy, Zoology, Health sciences, Agriculture and Forestry, Environmental sciences, Mathematics, Statistics, Animal Science, Bio Technology, Medical Sciences, Geology, Social Sciences, Natural sciences, Political Science, Urban Development academicians, professional, practitioners and students to impart and share knowledge in the form of high quality empirical and theoretical research papers etc. </p> IPHO Journal en-US IPHO-Journal of Advance Research in Applied Science 3050-9289 <p>Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties and that the Article has not been published elsewhere. Author(s) agree to the terms that the <strong>IPHO Journal</strong> will have the full right to remove the published article on any misconduct found in the published article.</p> https://iphopen.org/index.php/As/article/view/327 <p>This study introduces the Heavy-Tailed Kumaraswamy Half-LogisticG family of distributions, a flexible statistical framework for modeling<br>data with heavy-tailed behavior. The research explores its mathematical properties, estimation via maximum likelihood, and performance in<br>actuarial risk assessment. Monte Carlo simulations verify the consistency of parameter estimates, while numerical analyses evaluate some<br>key risk measures, demonstrating the model’s effectiveness in extremevalue modeling. A special case, the Heavy-Tailed Kumaraswamy HalfLogistic-Weibull distribution, is compared with relevant competing heavytailed models, proving its superior adaptability and precision. Realworld applications further validate its practicality in capturing complex<br>data patterns. The findings highlight the model’s robustness and relevance in actuarial science, finance, and risk analysis, offering a powerful tool for researchers and practitioners. By combining theoretical rigor,<br>computational validation, and empirical evidence, this work advances<br>statistical distribution theory and enhances modeling capabilities for<br>heavy-tailed phenomena.</p> Wilbert Nkomo Takesure Nyakuamba Joseph Manyemba Norah Chishamiso Gwesu Luba Gilberta Thwala Rita Sauriri Copyright (c) 2025 IPHO-Journal of Advance Research in Applied Science https://creativecommons.org/licenses/by-nc-sa/4.0 2025-08-21 2025-08-21 3 08 01 23 10.5281/zenodo.16917059