Akademik Veri Yönetim Sistemi
Mathematics, cilt.14, sa.3, 2026 (SCI-Expanded, Scopus)
The importance of statistical distributions in representing real-world scenarios and aidingin decision-making is widely acknowledged. However, traditional models often facelimitations in achieving optimal fits for certain datasets. Motivated by this challenge, thispaper introduces a new probability distribution termed the weighted sine generalizedKumaraswamy (WSG-Kumaraswamy) distribution. This model is constructed by integratingthe Kumaraswamy baseline distribution with the weighted sine-G family, whichincorporates a trigonometric transformation to enhance flexibility without adding extra parameters.Various statistical properties of the WSG-Kumaraswamy distribution, includingthe quantile function, moments, moment-generating function, and probability-weightedmoments, are derived. Maximum likelihood estimation is employed to obtain parameterestimates, and a comprehensive simulation study is performed to assess the finite-sampleperformance of the estimators, confirming their consistency and reliability. To illustrate thepractical advantages of the proposed model, two real-world datasets from epidemiologyand reliability engineering are analyzed. Comparative evaluations using goodness-of-fitcriteria demonstrate that the WSG-Kumaraswamy distribution provides superior fits comparedto established competitors. The results highlight the enhanced adaptability of themodel for unit-interval data, positioning it as a valuable tool for statistical modeling indiverse applied fields.