Revision as of 11:30, 20 July 2026

Abstract

The complexity of data necessitates the development of novel distributions. The introduction of new models allows us to enhance this data and remain contemporary. In this paper, we proposed and explored a new flexible distribution, referred to as the extended inverse Weibull model. This study presents a novel probability model, the extended inverse Weibull (E-IW) distribution. The recently introduced distribution combines the E-X approach with the inverse Weibull distribution. The novel distribution facilitates the evaluation of real-world data due to its analytical feasibility and applicability. The suggested distribution may accommodate several types of datasets. Numerous statistical characteristics of the proposed model were acquired. The features include quantile functions, ordinary moments, order statistics, and moment-generating functions. functions. The estimated parameters of the new model are determined using several estimation techniques, including maximum likelihood, least squares, weighted least squares, maximum product spacing, and the Bayesian method under the square error loss function. The simulation analysis performed on the suggested model validated the dependability and consistency of its parameters. Furthermore, actuarial measures were computed, along with simulation study for these actuarial metrics was executed. Five real data sets were taken from several sectors to illustrate the importance and usefulness of the proposed model. Our empirical findings underscore the significance of the recommended model as a flexible and reliable tool for statistical modeling, with implications for improving data-driven analyses and integrating parametric modeling into modern applications.OPEN ACCESS Received: 10/11/2025 Accepted: 27/01/2026 Published: 21/07/2026

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