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Re: Re: st: How to deal with the quasi-complete separation problem in the logit part of ZINB analysis
From
"smztsmzt" <[email protected]>
To
"statalist"<[email protected]>
Subject
Re: Re: st: How to deal with the quasi-complete separation problem in the logit part of ZINB analysis
Date
Mon, 1 Oct 2012 13:50:35 +0800
Hi JVerkuilen,
Thank you for your very helpful suggestions.
Best regards,
Pengpeng
发件人：JVerkuilen (Gmail)
发送时间：2012-09-30 22:14
主题：Re: st: How to deal with the quasi-complete separation problem in the logit part of ZINB analysis
收件人："statalist"<[email protected]>
抄送：
On Sun, Sep 30, 2012 at 2:18 AM, ��� <[email protected]> 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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