| MixedPoisson-package {MixedPoisson} | R Documentation |
Mixed Poisson Models
Description
The package provides functions, which support to fit parameters of different mixed Poisson models using the Expectation-Maximization (EM) algorithm of estimation, cf. (Ghitany et al., 2012, pp. 6848). In the model the assumptions are: conditional N|\theta is of distribution N|\theta \sim POIS(\lambda\theta), parameter \theta is a random variable distributed according to the density function f_{\theta}(\cdot), E[\theta]=1 and \lambda=\exp(\mathbf{x}_{i}'\mathbf{\boldsymbol \beta}) – the regression component. The E-step is carried out through the numerical integration using Laquerre quadrature. The M-step estimates the parameters \beta using GLM Poisson with pseudo values from E-step and mixing parameters using optimize function.
Details
| Package: | MixedPoisson |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2015-07-13 |
| License: | GPL-2 |
Author(s)
Alicja Wolny-Dominiak and Michal Trzesiok
Maintainer: <alicja.wolny-dominiak@ue.katowice.pl>
References
Karlis, D. (2005). EM algorithm for mixed Poisson and other discrete distributions. Astin Bulletin, 35(01), 3-24. Ghitany, M. E., Karlis, D., Al-Mutairi, D. K., & Al-Awadhi, F. A. (2012). An EM algorithm for multivariate mixed Poisson regression models and its application. Applied Mathematical Sciences, 6(137), 6843-6856.