Bibliometric Mapping of Zero-Inflated Statistical Models: Bibliometric Mapping of Zero-Inflated Statistical Models

Memduh AK; Cem TIRINK

doi:10.5281/zenodo.23124738

Authors

Memduh AK
Cem TIRINK

DOI:

https://doi.org/10.5281/zenodo.23124738

Keywords:

Bibliometric analysis, Zero-inflated models, Zero-inflated Poisson, Overdispersion, Count data, Science mapping

Abstract

In this study, the evolution of the scientific literature published on zero-inflated models was examined using bibliometric methods. Records obtained from the Web of Science Core Collection database were analyzed in the R environment using bibliometrix and related packages. The main analysis period was set as 1997–2025, and a total of 1,067 publications were evaluated. Annual scientific output, citation trends, leading sources, and authors were examined, along with keyword co-occurrence networks, temporal trends, emerging research topics, thematic structure, and reference co-citation networks. The findings indicate that the literature on zero-inflated models has grown rapidly, particularly after 2010, reaching its highest annual publication count in 2024. While zero inflation, overdispersion, zero-inflated Poisson, hurdle models, and the negative binomial model form the core conceptual framework of the field, Bayesian inference, marginalized models, INLA, and deep learning have emerged as topics gaining increasing prominence in recent years. Citation network analysis highlighted the central role of seminal works by Lambert, Mullahy, Hall, and Ridout in the field’s intellectual structure. The results indicate that zero-inflated models constitute a dynamic, interdisciplinary field of statistical research that continues to diversify methodologically.

References

Arab, A., Wildhaber, M. L., Wikle, C. K., & Gentry, C. N. (2008). Zero-inflated modeling of fish catch per unit area resulting from multiple gears: Application to channel catfish and shovelnose sturgeon in the Missouri River. North American Journal of Fisheries Management, 28(4), 1044–1058. https://doi.org/10.1577/M06-250.1

Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Bekalo, D. B., & Kebede, D. T. (2021). Zero-inflated models for count data: An application to number of antenatal care service visits. Annals of Data Science, 8, 683–708. https://doi.org/10.1007/s40745-021-00328-x

Böhning, D., Dietz, E., Schlattmann, P., Mendonça, L., & Kirchner, U. (1999). The zero-inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology. Journal of the Royal Statistical Society: Series A (Statistics in Society), 162(2), 195–209. https://doi.org/10.1111/1467-985X.00130

Boucher, J.-P., Denuit, M., & Guillén, M. (2007). Risk classification for claim counts: A comparative analysis of various zero-inflated mixed Poisson and hurdle models. North American Actuarial Journal, 11(4), 110–131. https://doi.org/10.1080/10920277.2007.10597487

Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Maechler, M., & Bolker, B. M. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal, 9(2), 378–400. https://doi.org/10.32614/RJ-2017-066

Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22, 155–205. https://doi.org/10.1007/BF02019280

Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data (2nd ed.). Cambridge University Press.

Chiogna, M., & Gaetan, C. (2007). Semiparametric zero-inflated Poisson models with application to animal abundance studies. Environmetrics, 18(3), 303–314. https://doi.org/10.1002/env.830

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011a). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402. https://doi.org/10.1002/asi.21525

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011b). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146–166. https://doi.org/10.1016/j.joi.2010.10.002

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

Feng, C. X. (2021). A comparison of zero-inflated and hurdle models for modeling zero-inflated count data. Journal of Statistical Distributions and Applications, 8, Article 8. https://doi.org/10.1186/s40488-021-00121-4

Hall, D. B. (2000). Zero-inflated Poisson and binomial regression with random effects: A case study. Biometrics, 56(4), 1030–1039. https://doi.org/10.1111/j.0006-341X.2000.01030.x

Hilbe, J. M. (2012). Negative binomial regression (2nd ed.). Cambridge University Press.

Isgin, T., Bilgic, A., Forster, D. L., & Batte, M. T. (2008). Using count data models to determine the factors affecting farmers’ quantity decisions of precision farming technology adoption. Computers and Electronics in Agriculture, 62(2), 231–242. https://doi.org/10.1016/j.compag.2008.01.004

Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25. https://doi.org/10.1002/asi.5090140103

Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14. https://doi.org/10.1080/00401706.1992.10485228

Lord, D., Washington, S. P., & Ivan, J. N. (2005). Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: Balancing statistical fit and theory. Accident Analysis & Prevention, 37(1), 35–46. https://doi.org/10.1016/j.aap.2004.02.004

Min, Y., & Agresti, A. (2005). Random effect models for repeated measures of zero-inflated count data. Statistical Modelling, 5(1), 1–19. https://doi.org/10.1191/1471082X05st084oa

Mullahy, J. (1986). Specification and testing of some modified count data models. Journal of Econometrics, 33(3), 341–365. https://doi.org/10.1016/0304-4076(86)90002-3

Preisser, J. S., Das, K., Long, D. L., & Divaris, K. (2016). Marginalized zero-inflated negative binomial regression with application to dental caries. Statistics in Medicine, 35(10), 1722–1735. https://doi.org/10.1002/sim.6804

Preisser, J. S., Stamm, J. W., Long, D. L., & Kincade, M. E. (2012). Review and recommendations for zero-inflated count regression modeling of dental caries indices in epidemiological studies. Caries Research, 46(4), 413–423. https://doi.org/10.1159/000338992

R Core Team. (2026). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Ridout, M., Demétrio, C. G. B., & Hinde, J. (1998). Models for count data with many zeros. In Proceedings of the 19th International Biometric Conference. Cape Town, South Africa.

Ridout, M., Hinde, J., & Demétrio, C. G. B. (2001). A score test for testing a zero-inflated Poisson regression model against zero-inflated negative binomial alternatives. Biometrics, 57(1), 219–223. https://doi.org/10.1111/j.0006-341X.2001.00219.x

Sidumo, B., Sonono, E., & Takaidza, I. (2024). Count regression and machine learning techniques for zero-inflated overdispersed count data: Application to ecological data. Annals of Data Science, 11, 803–817. https://doi.org/10.1007/s40745-023-00464-6

Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269. https://doi.org/10.1002/asi.4630240406

Tajuddin, R. R. M., & Ismail, N. (2025). Inflation properties in count data distributions: A three-decade bibliometric analysis. Sains Malaysiana, 54(1), 303–312. https://doi.org/10.17576/jsm-2025-5401-24

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84, 523–538. https://doi.org/10.1007/s11192-009-0146-3

Wilson, P. (2015). The misuse of the Vuong test for non-nested models to test for zero-inflation. Economics Letters, 127, 51–53. https://doi.org/10.1016/j.econlet.2015.01.002

Young, D. S., Roemmele, E. S., & Shi, X. (2022b). Zero-inflated modeling part II: Zero-inflated models for complex data structures. WIREs Computational Statistics, 14(2), Article e1540. https://doi.org/10.1002/wics.1540

Young, D. S., Roemmele, E. S., & Yeh, P. (2022a). Zero-inflated modeling part I: Traditional zero-inflated count regression models, their applications, and computational tools. WIREs Computational Statistics, 14(1), Article e1541. https://doi.org/10.1002/wics.1541

Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8), 1–25. https://doi.org/10.18637/jss.v027.i08

Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629

Bibliometric Mapping of Zero-Inflated Statistical Models