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Re: st: How to deal with the quasi-complete separation problem in the logit part of ZINB analysis

From   Nick Cox <>
Subject   Re: st: How to deal with the quasi-complete separation problem in the logit part of ZINB analysis
Date   Sun, 30 Sep 2012 15:42:03 +0100

That is a Jeffreys prior, or a Jeffreys' prior, as it was the idea of
Sir Harold Jeffreys.

In a letter to me in 1976 he wrote that he preferred the form
Jeffreys's, but this is rarely seen.


On Sun, Sep 30, 2012 at 3:14 PM, JVerkuilen (Gmail)
<> wrote:
> On Sun, Sep 30, 2012 at 2:18 AM,  <> wrote:
>> I encountered the quasi-complete separation problem in my data when I doing the ZINB analysis in Stata.
>> I have found and installed the firthlogit ADO file which is very useful to deal with the separation effect by using the PML method, but how could I use the same way in the ZINB analysis? Does any one know that there might be some similar package, command, parameters or ADO files that
>> could take the same effect in the ZINB analysis?
> In my experience the ZINB is quite challenging to fit. You have both
> negative binomial overdispersion and excess zeros. I'd try fitting a
> ZIP or simplify the ZI component of the model, assuming you are using
> some predictors for it.
> I'm sure with some programming the ideas in -firthlogit- could be
> extended to other models. Essentially from the math it looks like an
> approximation to a Jeffrey's prior in a fully Bayesian analysis.
> (Update: Went and looked at the paper by Firth and that's exactly what
> it is!) Other priors could similarly help. Often you can use a
> "pseudo-data" approach and add a few fake data cases to approximate a
> prior. Any recommendations beyond that would require some information
> about the dataset. If all your predictors are discrete you can often
> simply add a few observations to the cells created by the table. It
> might be to add a few cases with average covariate values and use MI
> to predict observations.
> Note that all these *tricks* are just that, tricks or devices that are
> not well-founded theoretically.
> Jay
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