Properties, actuarial measures and data modelling applications of the new heavy-tailed Kumaraswamy half-logistic-G family of distributions

2025-08-19T05:14:29+00:00

This study introduces the Heavy-Tailed Kumaraswamy Half-LogisticG family of distributions, a flexible statistical framework for modeling
data with heavy-tailed behavior. The research explores its mathematical properties, estimation via maximum likelihood, and performance in
actuarial risk assessment. Monte Carlo simulations verify the consistency of parameter estimates, while numerical analyses evaluate some
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
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,
computational validation, and empirical evidence, this work advances
statistical distribution theory and enhances modeling capabilities for
heavy-tailed phenomena.

IPHO-Journal of Advance Research in Applied Science

Properties, actuarial measures and data modelling applications of the new heavy-tailed Kumaraswamy half-logistic-G family of distributions