MixedPoisson-package: Mixed Poisson Models

In MixedPoisson: 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|θ is of distribution N|θ \sim POIS(λθ), parameter θ is a random variable distributed according to the density function f_{θ}(\cdot), E[θ]=1 and λ=\exp(\mathbf{x}_{i}'\mathbf{\boldsymbol β}) – the regression component. The E-step is carried out through the numerical integration using Laquerre quadrature. The M-step estimates the parameters β 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: <[e-mail]>

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.

MixedPoisson documentation built on May 2, 2019, 12:40 p.m.