## Correlated Poissons

A graduate student came to see me the other day with a bivariate Poisson distribution and a question about using EM in this framework. The problem boils down to adding one correlation parameter and an extra term in the likelihood $(1-\rho)^{n_1}(1+\lambda\rho)^{n_2}(1+\mu\rho)^{n_3}(1-\lambda\mu\rho)^{n_4}\quad 0\le\rho\le\min(1,\frac{1}{\lambda\mu})$

Both terms involving sums are easy to deal with, using latent variables as in mixture models. The subtractions are trickier, as the negative parts cannot appear in a conditional distribution. Even though the problem can be handled by a direct numerical maximisation or by an almost standard Metropolis-within-Gibbs sampler, my suggestion regarding EM per se was to proceed by conditional EM, one parameter at a time. For instance, when considering $\rho$ conditional on both Poisson parameters, depending on whether $\lambda\mu>1$ or not, one can consider either $(1-\theta/\lambda\mu)^{n_1}(1+\theta/\mu)^{n_2}(1+\theta/\lambda)^{n_3}(1-\theta)^{n_4}\quad0<\theta<1$

and turn $(1-\theta/\lambda\mu) \text{ into } (1-\theta+\theta\{1-\frac{1}{\lambda\mu}\})$

thus producing a Beta-like target function in $\theta$ after completion, or turn $(1-\lambda\mu\rho) \text{ into } (1-\rho+\{1-\lambda\mu\}\rho)$

to produce a Beta-like target function in $\rho$ after completion. In the end, this is a rather pedestrian exercise and I am still frustrated at missing the trick to handle the subtractions directly, however it was nonetheless a nice question!

### 2 Responses to “Correlated Poissons”

1. Arthur Says:

Actually, the bivariate Poisson regression has been programmed in the bivpois package, in R (as far as I remember, it was using that EM algorithm you mention). But for some reason, it cannot be installed anymore
http://cran.r-project.org/web/packages/bivpois/
“Package ‘bivpois’ was removed from the CRAN repository”
anyone knows why ? are there any problem with that package ?

• Corey Says:

I’ve been told that packages can drop out of CRAN when a new version of R is released. The CRAN people run their suite of tests, and a package fails to pass, then it is moved from the repository to the archive. It is the responsibility of the package maintainer, if any, to get the package back into the repository.

Archived packages can still installed with a little work.

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