Three-level zero-inflated Conway–Maxwell–Poisson regression model for analyzing dispersed clustered count data with extra zeros
Author:
Funder
Hamadan University of Medical Sciences
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s13571-020-00229-8.pdf
Reference21 articles.
1. Almasi, A., Eshraghian, M. R., Moghimbeigi, A., Rahimi, A., Mohammad, K., & Fallahigilan, S. (2016). Multilevel zero-inflated Generalized Poisson regression modeling for dispersed correlated count data. Statistical Methodology, 30, 1-14, https://doi.org/10.1016/j.stamet.2015.11.001.
2. Barriga, G. D., & Louzada, F. (2014). The zero-inflated Conway–Maxwell–Poisson distribution: Bayesian inference, regression modeling and influence diagnostic. Statistical Methodology, 21, 23-34, https://doi.org/10.1016/j.stamet.2013.11.003.
3. Choo-Wosoba, H., & Datta, S. (2018). Analyzing clustered count data with a cluster-specific random effect zero-inflated Conway–Maxwell–Poisson distribution. Journal of Applied Statistics, 45(5), 799-814, https://doi.org/10.1080/02664763.2017.1312299.
4. Francis, R. A., Geedipally, S. R., Guikema, S. D., Dhavala, S. S., Lord, D., & LaRocca, S. (2012). Characterizing the performance of the conway-maxwell poisson generalized linear model. Risk Analysis: An International Journal, 32(1), 167-183, https://doi.org/10.1111/j.1539-6924.2011.01659.x.
5. Guikema, S. D., & Goffelt, J. P. (2008). A flexible count data regression model for risk analysis. Risk Analysis: An International Journal, 28(1), 213-223, https://doi.org/10.1111/j.1539-6924.2008.01014.x.
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