I was speaking to freq in bin. For example a distribution of homicide counts in 200 cities that ranges from 0 to 350.
Thanks for the clarification. I'll also check on Scott Long's book. I like the idea of comparing estimation methods. Unfortunately there is a trend in the social sciences for reviewers to assume one has to use a specific model - because it's the new convention - even when it is unnecessary. Criminology and sociology are plagued by this trend.
-----Original Message-----
From: [email protected]
[mailto:[email protected]]On Behalf Of Nick Cox
Sent: Wednesday, October 13, 2004 5:22 PM
To: [email protected]
Subject: st: RE: RE: RE: Negative Binomial Models
Are we talking about #bins or max(freq in bin) ?
Nick
[email protected]
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]]On Behalf Of MacDonald,
> John
> Sent: 14 October 2004 00:58
> To: [email protected]
> Subject: st: RE: RE: Negative Binomial Models
>
>
> Thanks Nick.
>
> John
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]]On Behalf Of Nick Cox
> Sent: Wednesday, October 13, 2004 4:44 PM
> To: [email protected]
> Subject: st: RE: Negative Binomial Models
>
>
> 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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