The Marshall–Olkin family of distributions has gained increasing attention in fields such as reliability engineering, survival analysis, financial risk modeling, and actuarial science because of its flexibility in modeling dependence among events and its wide range of extensions. Despite its growing relevance, a systematic understanding of how research on Marshall–Olkin models has evolved over time is still limited. This study addresses this gap by combining bibliometric techniques with topic modeling to analyze the structure and evolution of the scientific literature on Marshall–Olkin models. The analysis includes all 266 peer-reviewed publications on Marshall–Olkin models indexed in Scopus between 1981 and 2025. Bibliometric techniques (including heatmaps, clustering analyses, and temporal visualizations) are used to characterize publication patterns, source relationships, and thematic evolution. In addition, Latent Dirichlet Allocation (LDA) uncovered 27 topics and examined their prevalence across journals and time periods. The results reveal five main clusters of publication sources and three temporal groupings derived from hierarchical clustering of topic distributions, reflecting the thematic progression of the field. Overall, the findings highlight both the persistence of core research themes and the emergence of new applications, particularly in areas such as Bayesian competing risks, censoring models, and parameter estimation in Weibull-based frameworks. This study provides a systematic and data-driven perspective on the intellectual evolution of Marshall–Olkin research, helping scholars identify emerging trends and potential directions for future work.
Llinas et al. (Sat,) studied this question.