I can heartily recommend this article - it's interesting and
informative. What I'd like to see, also is something about the form of
the output. I've found that the best program in the world (whatever
that may be!) isn't helpful if you have to search the output for what
you want and that process is susceptible to errors, especially for
beginners.
I'm thinking of issues related to hierarchical models and comparing
Stata results to Mplus results. Stata is superior to Mplus on the
output score.
Tony
Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of
[email protected]
Sent: Saturday, February 16, 2008 7:14 AM
To: [email protected]
Subject: st: RE: Exact Poisson Regression
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
**************The year's hottest artists on the red carpet at the Grammy
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