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st: RE: RE: convergence problems with zinb

From   "Holland, Margaret" <[email protected]>
To   <[email protected]>
Subject   st: RE: RE: convergence problems with zinb
Date   Thu, 14 Aug 2008 12:29:23 -0400

Thanks for the helpful suggestions.

I've tried a few of the different algorithms, with and without the
"difficult" option and I'm getting closer (I think). I don't get any
error messages, it looks like it converges, and I get coefficient
estimates, but some of the standard errors are missing (or huge):

inflate      |
     chronic |  -11.51164        .      .     .          .         .
  asthmbronc |  -26.67018 193706.5  -0.00 1.000  -379684.4  379631.1

I'm interpreting this as there was a problem in the estimation, but for
some reason it didn't trigger an error message. Is this an appropriate?
I'll continue working to refine the model based on that assumption, but
was curious if there was anything else I should know.

I have not yet taken the time to explore potential problems with my
imputed data, but I suspect the problem is more in the model itself
because I have seen the same problem in my raw (not imputed) data.
However, I will take a look as suggested, just in case there is
something unusual. I should probably have done that by now anyway...

Thanks again for the help on this,

-----Original Message-----
Sent: Wednesday, August 13, 2008 8:04 PM
Subject: st: RE: convergence problems with zinb

Hi Margaret,

You might like to look at the various maximize options that you can
tinker with.  

Type - help maximize

There are four different algorithms used by ml and Stata follows a rule
for stepping though these when fitting a model.  There is an option
called - difficult - which tells the ml program to use a different
stepping rule.  You could try this.

Any algorithm used to maximize the log-likelihood has to start with some
initial coefficient values and sometimes these can be near a flat or
concave region of the likelihood function.  Consequently, the fitting
algorithm will just wander around this region, unable to get out.
Changing the initial values might therefore improve the performance of
the algorithm.

The option - trace - will give you the current coefficient estimates at
each iteration.  This might give you a clue as to what's going on. 

I don't know how many variables you have in your model, but you could
try fitting a separate model for each variable thus obtaining a
coefficient estimate for each variable and then use these as your
initial estimates in the full model.

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