Mapping Research on the Birnbaum–Saunders Statistical Distribution: Patterns, Trends, and Scientometric Perspective

This article provides a critical assessment of the Birnbaum–Saunders (BS) distribution, a pivotal statistical model for lifetime data analysis and reliability estimation, particularly in fatigue contexts. The model has seen successfully applied across diverse fields, including biological mortality, environmental sciences, medicine, and risk models. Moving beyond a basic scientometric review, this study synthesizes findings from 353 peer-reviewed articles, selected using PRISMA 2020 protocols, to specifically trace the evolution of estimation techniques, regression methods, and model extensions. Key findings reveal robust theoretical advances, such as Bayesian methods and bivariate/spatial adaptations, alongside practical progress in influence diagnostics and software development. The analysis highlights key research gaps, including the critical need for scalable, auditable software and structured reviews, and notes a peak in scholarly activity around 2019, driven importantly by the Brazil-Chile research alliance. This work offers a consolidated view of current BS model implementations and outlines clear future directions for enhancing their theoretical robustness and practical utility.

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Publication Details

Journal
Stats
Published
2025-12-13
DOI
https://doi.org/10.3390/stats8040116
Citations
1
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
2.63
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article

Mapping Research on the Birnbaum–Saunders Statistical Distribution: Patterns, Trends, and Scientometric Perspective

Víctor Leiva
1 citations
Stats
Statistical Distribution Estimation and Applications
2.63
article

Mapping Research on the Birnbaum–Saunders Statistical Distribution: Patterns, Trends, and Scientometric Perspective

Víctor Leiva
article en
1 citations

Abstract

This article provides a critical assessment of the Birnbaum–Saunders (BS) distribution, a pivotal statistical model for lifetime data analysis and reliability estimation, particularly in fatigue contexts. The model has seen successfully applied across diverse fields, including biological mortality, environmental sciences, medicine, and risk models. Moving beyond a basic scientometric review, this study synthesizes findings from 353 peer-reviewed articles, selected using PRISMA 2020 protocols, to specifically trace the evolution of estimation techniques, regression methods, and model extensions. Key findings reveal robust theoretical advances, such as Bayesian methods and bivariate/spatial adaptations, alongside practical progress in influence diagnostics and software development. The analysis highlights key research gaps, including the critical need for scalable, auditable software and structured reviews, and notes a peak in scholarly activity around 2019, driven importantly by the Brazil-Chile research alliance. This work offers a consolidated view of current BS model implementations and outlines clear future directions for enhancing their theoretical robustness and practical utility.

StatsVol. 8(4)
Pontificia Universidad Católica de Valparaíso (CL)
Openalex Percentile: Top 7%
Statistical Distribution Estimation and Applications
2.63
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