MixedPoisson-package:
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 |
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Type: |
| Package |
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Version: |
| 1.0 |
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Date: |
| 2015-07-13 |
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License: |
| GPL-2 |
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.