# Overdispersion parameter

I am modelling a zero-truncated process with a count model, and am trying to determine whether the data are overdispersed. The Poisson distribution has a variance equal to its mean, $$\newcommand{\Var}{\operatorname{Var}} \Var(y) = E(y) = \lambda$$ The negative binomial model relaxes this assumption by estimating an overdispersion parameter $\alpha$. There are two regimens for this method, NB1 $$\Var(y) = \lambda \alpha$$ and NB2 $$\Var(y) = \lambda (1 + \lambda/\alpha)$$ I have seen the second version more often. I have estimated three models with the same covariates but the three different link functions (Poisson, NB1, NB2).

Model   logLik   alpha  p(alpha)
Poisson -7942    1      NA
NB1     -6399    1.001  0
NB2     -7944    403.4  0


Clearly the overdispersion parameter is significant and I should therefore use one of the NB models. But how can the log-likelihood for NB2 be lower than for Poisson when there is an additional parameter in the model?

When I estimate all three models, I get a warning that my variance-covariance matrix may not be positive semi-definite. But I don't have highly collinear variables, and all of the relevant matrices have strictly positive eigenvalues, so I don't see why I'm getting this warning. Could this be related to the likelihood problem?

I am using the glmmadmb function from the R package of the same name.

• I would recommend that you also try to estimate the variance-covariance matrix of the Poisson estimates using the Huber/White/Sandwich linearized estimator. That estimator of the variance-covariance matrix does not assume $E(y_i) = Var(y_i)$, nor does it even require that $Var(y_i)$ be constant across $i$. That would be less of apples-to-orangutangs comparison with the NB model. I can't tell if glmmadmb allows you to calculate the errors that way or not. Mar 29, 2013 at 20:45

You are right, the NB2 model should not have a worse fit than the poisson model, because the latter is a special case of the former, where $\alpha=0$. There is definitely a problem. It sounds to me like your numerical maximum likelihood maximizer had problems with your specification and therefore didn't really hit the global maximum.