Should or should not?
I'd like to hear the grounds for that. I find it
difficult to believe that any such criterion tenet
aquam. You should use a negative binomial distribution
whenever fitted probabilities are close to observed
probabilities.
The underlying frequencies are not that germane.
But if the total number of individuals in the sample
is large, at least some cell frequencies are likely
to be. In fact, to get a good handle on probabilities
in the tail, a very large sample is a really good idea.
I'm interested in comments on this. If you type
. ssc type nbfit.hlp
there's a worked example in which the peak frequency
was 807.
Nick
[email protected]
> -----Original Message-----
> From: Nick Cox
> Sent: 14 October 2004 00:38
> To: '[email protected]'
> Subject: RE: Negative Binomial Models
>
>
>
>
> Nick
> [email protected]
>
> > -----Original Message-----
> > From: [email protected]
> > [mailto:[email protected]]On Behalf Of MacDonald,
> > John
> > Sent: 14 October 2004 00:23
> > To: [email protected]
> > Subject: st: Negative Binomial Models
> >
> >
> > Is there a maximum number of counts one should not exceed
> > when using the negative binomial model? Even if there is a
> > adjustment for exposure (e.g., rate of homicides in a city).
> > I have a data set with over 200 homicides in some cities and
> > I saw a reference in an article of American Journal of
> > Sociology to Maddala's 1977 econometrics textbook that said
> > you should exceed 50 counts. Is this true?
> >
> >
> >
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