Evolution and Research Trends in Truncated Distribution Studies: A Bibliometric and Meta-Analytical Review

In empirical studies, data are often truncated because of institutional limits, measurement problems, or sampling methods, which means observations are limited to a certain range. If truncation isn't considered, parameter estimates can be skewed, and statistical conclusions can be wrong. Because of this, truncated probability distributions are needed to accurately model real-world data. This paper gives a bibliometric review of studies on truncated distributions to show the field's history, structure, and how its topics have changed. Data from Scopus between 2000 and 2025 was gathered and examined using the Bibliometrix package in R. Publication trends, key authors and sources, cooperation, and new subjects were assessed using descriptive stats and science-mapping methods like co-authorship, co-citation, keyword co-occurrence, topic mapping, and historical analysis. The results show consistent growth in scientific output, high citation rates for key theoretical work, and more interdisciplinary use in fields like economics, medical statistics, survival analysis, reliability engineering, and finance. The results show a research area that is both well-established and still changing, with solid theoretical bases, more practical uses, and more global teamwork. This work gives a structured look at research on truncated distributions and provides helpful information for people who want to find key studies, main subjects, and future research directions in the study of truncated data.