Bibliometric analysis of research on firth penalized logistic regression in addressing complete separation
Complete separation in logistic regression leads to infinite estimates which prevents reliable inference. Firth's penalized likelihood method has emerged as a widely accepted and reliable solution that provides finite and more stable estimates. Despite its growing relevance, a thorough understa...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
UiTM Cawangan Perlis
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/131386/1/131386.pdf https://doi.org/10.24191/jcrinn.v10i2.535 https://ir.uitm.edu.my/id/eprint/131386/ https://jcrinn.com/index.php/jcrinn |
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| Summary: | Complete separation in logistic regression leads to infinite estimates which prevents reliable inference. Firth's penalized likelihood method has emerged as a widely accepted and reliable solution that provides finite and more stable estimates. Despite its growing relevance, a thorough understanding of the global research on this topic remains limited. This study conducts a bibliometric analysis of trends related to complete separation in logistic regression using Firth penalized regression. Bibliographic data were retrieved from the Scopus database and analysed using Microsoft Excel and VOSviewer software. After applying inclusion criteria, nine journal articles published between 2012 and 2024 were identified through a structured search conducted on February 22, 2025. The findings reveal a small but growing body of literature, reflecting the emerging status of research on complete separation in logistic regression using Firth penalized regression. The results show an upward trend in publications, particularly from 2019 onward with the United States and Malaysia identified as the most productive countries. Influential articles contributed to methodological development and applications in health and transportation research. Keyword co-occurrence analysis identified thematic clusters in human studies, statistical modelling, and estimation techniques. These findings provide an overview of publication trends, collaboration networks, and research gaps which could support future methodological and multidisciplinary integration of Firth penalized regression. |
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