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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" <smztsmzt@163.com>
To   "statalist"<statalist@hsphsun2.harvard.edu>
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"<statalist@hsphsun2.harvard.edu>
抄送:

On Sun, Sep 30, 2012 at 2:18 AM, ��� <smztsmzt@163.com> 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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