Comparing Some Negative Binomial Regression with Simulation

Authors

DOI:

https://doi.org/10.56294/sctconf2024.1127

Keywords:

Negative Binomial Regression, Estimation Method, Binomial Distribution

Abstract

This paper presented a comprehensive comparative study of various negative binomial regression models using simulation techniques. Negative binomial regression models are widely employed in statistical analyses, particularly in situations involving count data with over dispersion. Zero amplified binomial distribution regressions are considered as important regressions because they have many applications for many experiments. The research aims to compare the two estimation methods (moments Estimation method, Maximum likelihood Estimation method and shrinkage estimation method) for zero inflation for a binomial distribution through a number of experiments with deference (random sample size, value of the distribution parameters and the estimation method). Our findings aim to provide practitioners and researchers with valuable insights into the strengths and limitations of different negative binomial regression models. By understanding the relative performance of these models through simulation studies

References

1- Neamah, M. W., & Raheem, S. H. (2021). Comparing Poisson Regression via Negative Binomial Regression for Modeling Zero-Inflated Data. International Journal of Agricultural & Statistical Sciences, 17(1).

2-AL-Mosawy, Z. A. A., & AL-Tai, A. H. H. (2023). Compare some Estimation Methods for Zero-Inflated Poisson Regression Models With Simulation. International Journal of Nonlinear Analysis and Applications, 14(1), 1787-1793.

3-da Silva, A. R., & de Sousa, M. D. R. (2023). Geographically Weighted Zero-Inflated Negative Binomial Regression: A general Case for Count Data. Spatial Statistics, 58, 100790.

4- Kadhim MT, Chilab AN. The effect of the formal organizer strategy on the achievement and visual thinking skills of first-year intermediate female students in social studies subject. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Jan. 1 [cited 2024 Aug. 21];3:962. Available from: https://conferencias.ageditor.ar/index.php/sctconf/article/view/829

5-He, Q., & Huang, H.-H. (2024). A Framework of Zero-Inflated Bayesian Negative Binomial Regression Models for Spatiotemporal Data. Journal of Statistical Planning and Inference, 229, 106098.

6-Hilbe, J. M. (2011). Negative Binomial Regression: Cambridge University Press.

7-Saengthong, P., Bodhisuwan, W., & Thongteeraparp, A. (2015). The Zero Inflated Negative Binomial–Crack Distribution: Some Properties and Parameter Estimation. Songklanakarin J. Sci. Technol, 37(6), 701-711.

8-Schmeiser, B. (1990). Simulation Experiments. Handbooks in Operations Research and Management Science, 2, 295-330.

9-Welch, P. D. (1983). The Statistical Analysis of Simulation Results. The Computer Performance Modeling Handbook, 22, 268-328.

10-Yu, D., Huber, W., & Vitek, O. (2013). Shrinkage Estimation of Dispersion in Negative Binomial Models for RNA-Seq Experiments with Small Sample Size. Bioinformatics, 29(10), 1275-1282.

11-Zhou, M., Li, L., Dunson, D., & Carin, L. (2012). Lognormal and Gamma Mixed Negative Binomial Regression. Paper presented at the proceedings Of The... International Conference on Machine Learning. International Conference on Machine Learning.

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Published

2024-08-28

How to Cite

1.
Ali AL-Mosawy ZA, Habeeb NA. Comparing Some Negative Binomial Regression with Simulation. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Aug. 28 [cited 2024 Oct. 8];3:.1127. Available from: https://conferencias.ageditor.ar/index.php/sctconf/article/view/1127