Work

Classical and Bayesian Methodology for a New Inverse Statistical Model

Year: 2025

Type: article

Abstract: ABSTRACT This article introduces a two‐parameter statistical model derived by applying an inverse transformation to the cumulative distribution function of the Pham distribution. The proposed model offers a flexible and tractable framework for modeling skewed and heavy‐tailed data, making it well‐suited for applications in reliability engineering, survival analysis, and related fields. We derive key statistical properties of the model, including the quantile function, moments, and the moment‐generating function. Furthermore, we assess the performance of fifteen different estimation methods through extensive simulations to identify the most efficient techniques for parameter estimation. The practical utility of the proposed model is demonstrated using real‐life datasets, where it outperforms several existing competing models. Also, Bayesian inference is implemented in the application section to provide a more comprehensive analysis. The results underscore the model's flexibility, robustness, and computational efficiency in real‐world settings.

Source: Engineering Reports

DOI: 10.1002/eng2.70323

Authors: Ibrahim Elbatal, Mustafa Ibrahim Ahmed Araibi, Selasi Kwaku Ocloo, Ehab M. Almetwally, Laxmi Prasad Sapkota, Ahmed M. Gemeay

Institutions: Imam Mohammad ibn Saud Islamic University, Ashesi University, Tribhuvan University, Tanta University

Language: English

Topic: Statistical Distribution Estimation and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funder: Imam Mohammed Ibn Saud Islamic University