Marshall-Olkin distributions: a bibliometric study

Recently, there has been a growing interest among statistical researchers to develop new probability distributions, adding one or more parameters to previously existing ones. In this article, we describe a bibliometric analysis carried out to show the evolution of various models based on the seminal model of (Marshall and Olkin, Biometrika. 1997). This method allows us to explore and analyze large volumes of scientific data through performance indicators to identify the main contributions of authors, universities, and journals in terms of productivity, citations, and bibliographic coupling. The analysis was performed using the Bibliometrix R-package tool. The sample of analyzed data was based on articles indexed in the main collection of the Web of Science and Scopus between 1997 and 2021. This work also includes an overview of the methodology used, the corresponding quantitative analyses, a visualization of networks of collaboration and co-citations, as well as a description of the topic trends. In total, 131 articles were analyzed, which were published in 67 journals. Two journals published 17% of the manuscripts analyzed in this work. We identified 238 separate authors who have participated in the development of this research topic. In 2020, 49% of the authors presented new distributions, where their proposed models included up to six parameters. The publications were grouped into 20 collaborative groups of which Groups 1 and 2 are dominant in the development of new models. Thus, 24% of the publications analyzed belong to two researchers who lead these two groups. To show the flexibility of the new distributions, the authors apply their models using at least two sets of real-world data to show their potentiality. This article gives a broad overview of different generalizations of the Marshall-Olkin model and will be of great help to those interested in this line of research.