Dear Tracy,
a(hopefully) useful clue to address the issue you outlined may come up from
the below reported Joseph M. Hilbe's answer to Gary Anderson (please see
Statalist threads posted on Feb 16, 2008).
Besides, Joseph M Hilbe is the author of the very interesting textbook
Negative Binomial Regression. Cambridge, 2007 (with a lot of examples
carried out using Stata).
Sorry I cannot be more helpful.
Kind Regards,
Carlo
........................................................................
Gary Anderson asks about whether anyone has developed an exact negative
binomial command. No one has done that yet, but the folks at Cytel talked to
me about it back in November 2005 when I gave an ASA LearnStat course in
the Boston area.
The parameterization of the exact negative binomial would take the
canonical form; ie it would not be the Poisson-gamma mixture model
parameterization with which most statisticians are familiar. Therefore, it
would not have the same relationship to Poisson overdispersion as does the
NB-2 type of negative binomial, which is estimated by using the default form
of -nbreg-. The canonical negative binomial can be used to model count data,
and does a good job modeling data that is Poisson-overdispersed. I say this
because negative binomial models can be overdispersed as well. But, because
it does not have the log link as does Poisson (and NB-2), the canonical NB
heterogeneity or ancillary parameter it cannot be used for direct
comparisons with Poisson overdispersion as is NB-2. Again, an exact NB would
be a canonical NB.
I submitted a maximum likelihood canonical NB Stata program to SSC last
year called -cnbreg-.
It has all of the bells and whistles as the usual Stata maximum likelihood
commands. I've been doing simulation studies on the NB-C model, as I call
the canonical NB in "Negative Binomial Regression", comparing it with
Poisson, NB-2, and NB-1 models. I intend to publish the results when
completed.
NB-C is actually a nice model and can do a better job modeling some types
of data than NB-2 or NB-1.
I think it is worth the effort to construct an exact NB command, but I now
doubt that Cytel will get to it.
LogXact, Cytel's software application for modeling exact logistic and exact
Poisson models, is not alone any more in providing this capability to its
users. SAS and SPSS can model exact logistic models, and Stata both exact
logistic and exact Poisson. Because of the strong competition in this
regard, it is my understanding that Cytel is emphasizing development of
packages such as East, which is marketed to the clinical trials industry. I
doubt that it will develop an exact NB now. And since there are no
published algorithms showing how to do it, I very much doubt that SAS or
SPSS will take it on. That leaves Stata Corp. An exact NB, although of
canonical parameterization, still would be valuable for modeling counts
with excessive correlation in the data. There are great reasons why I think
it worth the effort.
By the way, Bob Oster and I wrote an article for "The American
Statistician"
(current issue) which compares the exact statistics capabilities of
StatXact/LogXact, SAS, SPSS, and Stata. Those of you who have an interest in
exact statistics may find the review to be helpful.
Joseph Hilbe
............................................................................
-----Messaggio originale-----
Da: [email protected]
[mailto:[email protected]] Per conto di Tracy Clegg
Inviato: luned� 18 febbraio 2008 16.37
A: [email protected]
Oggetto: st: exact poisson regression
Could anyone tell me whether there is a way to assess the goodness of fit
for an exact poisson regression model?
In addition does anyone know of a way to compare non-nested exact poisson
regression models? I have been using STATA to carry out the analysis.
Any help would be much appreciated.
Tracy Clegg
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