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Re: st: convergence problems with zinb

From   Maarten buis <[email protected]>
To   [email protected]
Subject   Re: st: convergence problems with zinb
Date   Wed, 13 Aug 2008 23:37:55 +0100 (BST)

--- "Holland, Margaret" <[email protected]> wrote:
> I believe a zero-inflated negative binomial would be the best fit for
> a model I am trying to run, but I've had trouble with convergence. I
> was wondering if anyone else has had this problem and, if so, if
> are any tricks to helping it converge or ways to try a different
> algorithm.
> I have tried running the same model in R and SAS with less success. I
> have done some imputation for this project and found a single
> imputation set that will converge on Stata, but will not converge on
> R or SAS. Based on this set, zinb is a better fit than zip or nbreg.
> I have found that a hurdle model (logit / zero-truncated negative
> binomial) converges more easily and has only slight worse fit than
> the zinb in the set that will converge. However, theoretically the
> zinb model makes more sense.

I would first do some scatter plots of observed and imputed values for
each imputation dataset and see if the dataset in which -zinb-
converges is substantively different from the others, and see if your
imputations make sense. Multiple Imputation of this type of data for
this type of models could easily go wrong, and the non-convergence may
be a blessing in disguise by alerting you to that problem. If the data
seems ok, you can use the estimates from the dataset that converges as
starting values for the other datasets.

-- Maarten

Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands

visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434

+31 20 5986715

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