Evolution of Research on Inverse Continuous Probability Distributions: A Bibliometric Study

Abstract

Inverse continuous probability distributions have become increasingly important in statistical modelling because of their ability to represent lifetime, reliability and survival data with greater flexibility than many classical distributions. As research in this area has expanded over the past five decades, the literature has become widely dispersed, making it difficult to identify major research trends and influential contributions. This study provides a comprehensive bibliometric analysis of 476 publications indexed in the Scopus database between 1968 and 2026. Using the Bibliometrix package in R together with science-mapping techniques, the study examines publication trends, citation patterns, leading authors, journals, institutions, countries and research collaborations. The analysis reveals a steady increase in scientific output, growing international collaboration and an expanding range of applications in reliability engineering, survival analysis, Bayesian statistics, finance and environmental modelling. It also highlights the evolution of the field from the development of classical inverse continuous probability distributions to more flexible generalized models and computational approaches. By mapping the intellectual structure and emerging research themes, this study provides a clear overview of the field's development and identifies promising directions for future research on inverse continuous probability distributions.