On the Half-Logistic New Weibull Pareto Distribution with Applications to Lifetime and Reliability Data

This paper introduces a novel probability distribution, the \textit{Half Logistic New Weibull-Pareto (HLNWP)} distribution, derived by applying a half logistic transformation to the New Weibull-Pareto family. The proposed distribution generalizes several existing models and offers enhanced flexibility in modeling diverse data behaviors. We derive and discuss key statistical properties including the probability density function, cumulative distribution function, hazard and reverse hazard functions, quantile function, moments, moment generating function, probability weighted moments, and order statistics. The maximum likelihood estimation (MLE) method is employed for parameter estimation, and analytical expressions for the score vector are provided. A comprehensive Monte Carlo simulation study is conducted to assess the performance and consistency of the MLEs. Finally, real-world data sets are analyzed to illustrate the applicability and superiority of the HL-NWP model in comparison with other non-nested alternatives. The results demonstrate the robustness and adaptability of the HL-NWP distribution for practical data modeling scenarios.