فهرست نشریات علمی دسترسی آزاد ایران

The Pareto distribution is a cornerstone for modeling heavy-tailed phenomena in fields like economics, finance, and risk management. While maximum likelihood (ML) estimation is prevalent, its estimators lack closed-form expressions, requiring iterative numerical methods. This paper introduces closed-form estimators for the Pareto distribution using the method of logarithmic moments. The proposed estimators are computationally simple and eliminate convergence issues associated with ML. We derive their large-sample properties, establishing consistency. For comparative purposes, L-moments estimators for the Pareto distribution are also considered. A comprehensive simulation study demonstrates that the proposed logarithmic moments estimators perform competitively with ML for heavy-tailed distributions. Moreover, an empirical application to real-world fire insurance claims data confirms the practical advantages of the proposed method, where it achieves a better fit compared to ML. The combination of closed-form simplicity, competitive performance in heavy-tailed settings, and computational efficiency makes the proposed estimators a powerful and practical alternative for rapid data analysis, pedagogical purposes, and applications with limited computational resources.