A Novel Inverse Statistical Model for Lifetime and Reliability Analysis

ABSTRACT This paper introduces the Inverse Mustapha Type‐II Distribution (), a novel lifetime model derived through the inverse transformation of the Mustapha Type‐II distribution. Designed for modeling data on the positive real line, the offers enhanced flexibility in reliability and survival analysis, particularly where traditional models fall short. We derive key statistical properties of the distribution, including moments, mean residual life, mean inactivity time, and entropy measures. Parameter estimation is explored using multiple methods, with simulation studies demonstrating the robustness of the estimators. The practical applications of the are validated through applications to real‐world datasets, where it outperforms competing lifetime models in goodness‐of‐fit tests. Our results highlight the as a valuable tool for reliability analysis, survival studies, and related fields, providing a versatile alternative for practitioners.

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Publication Details

Journal
Engineering Reports
Published
2025-08-01
DOI
https://doi.org/10.1002/eng2.70335
Citations
1
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
2.63
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article

A Novel Inverse Statistical Model for Lifetime and Reliability Analysis

Ibrahim E. Ragab, Selasi Kwaku Ocloo, Ahmed M. Gemeay
1 citations
Engineering Reports
Statistical Distribution Estimation and Applications
2.63
article

A Novel Inverse Statistical Model for Lifetime and Reliability Analysis

Ibrahim E. Ragab, Selasi Kwaku Ocloo, Ahmed M. Gemeay
article en
1 citations

Abstract

ABSTRACT This paper introduces the Inverse Mustapha Type‐II Distribution (), a novel lifetime model derived through the inverse transformation of the Mustapha Type‐II distribution. Designed for modeling data on the positive real line, the offers enhanced flexibility in reliability and survival analysis, particularly where traditional models fall short. We derive key statistical properties of the distribution, including moments, mean residual life, mean inactivity time, and entropy measures. Parameter estimation is explored using multiple methods, with simulation studies demonstrating the robustness of the estimators. The practical applications of the are validated through applications to real‐world datasets, where it outperforms competing lifetime models in goodness‐of‐fit tests. Our results highlight the as a valuable tool for reliability analysis, survival studies, and related fields, providing a versatile alternative for practitioners.

Engineering ReportsVol. 7(8)
Tanta University (EG), Imam Mohammad ibn Saud Islamic University (SA), Islamic University (BD), Ashesi University (GH), Egyptian Institute of Alexandria Academy for Administration & Accounting (EG)
Openalex Percentile: Top 10%
Statistical Distribution Estimation and Applications
2.63
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