References
- Consul, P. C. (1989). Generalized Poisson Distribution: Properties and Applications. New York: Marcel Dekker.
- Consul, P. C., Famoye, F. (2006). Lagrangian Probability Distributions. Boston, MA: Birkhauser.
- Consul, P. C., Shoukri, M. M. (1988). Some chance mechanisms related to a generalized Poisson probability model. Communications in Statistics– Simulation and Computation 14:667–681.
- Demirtas, H. (2004). Simulation-driven inferences for multiply imputed longitudinal datasets. Statistica Neerlandica 58:466–482.
- Famoye, F. (1997). Generalized Poisson random variate generation. American Journal of Mathematical and Management Sciences 17:219–237.
- Joe, H., Zhu, R. (2005). A method for generating high-dimensional multivariate binary variates. Biometrical Journal 47:219–229.
- Kemp, A. W. (1981). Efficient generation of logarithmically distributed pseudo-random variables. Applied Statistics 30:249–253.
- R Development Core Team. (2016). R: A Language and Environment for Statistical Computing. Available at: http://cran.r-project.org R Foundation for Statistical Computing, Vienna, Austria.
- Yahav, I., Shmueli, G. (2012). On generating multivariate Poisson data in management science applications. Applied Stochastic Models in Business and Industry 28:91–102.
- Yee, T. W. (2016). Vector Generalized Linear and Additive Models. R package VGAM. Available at: http://www.cran.r-project.org/web/packages/VGAM. R Foundation for Statistical Computing, Vienna, Austria.