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st: RE: RE: RE: Negative Binomial Models


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: RE: RE: Negative Binomial Models
Date   Thu, 14 Oct 2004 01:21:56 +0100

Are we talking about #bins or max(freq in bin) ? 

Nick 
n.j.cox@durham.ac.uk 

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of MacDonald,
> John
> Sent: 14 October 2004 00:58
> To: statalist@hsphsun2.harvard.edu
> Subject: st: RE: RE: Negative Binomial Models
> 
> 
> Thanks Nick. 
> 
> John
> 
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Nick Cox
> Sent: Wednesday, October 13, 2004 4:44 PM
> To: statalist@hsphsun2.harvard.edu
> 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 
> n.j.cox@durham.ac.uk 
> 
> > -----Original Message-----
> > From: Nick Cox 
> > Sent: 14 October 2004 00:38
> > To: 'statalist@hsphsun2.harvard.edu'
> > Subject: RE: Negative Binomial Models
> > 
> > 
> > 
> > 
> > Nick 
> > n.j.cox@durham.ac.uk 
> > 
> > > -----Original Message-----
> > > From: owner-statalist@hsphsun2.harvard.edu
> > > [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of 
> MacDonald,
> > > John
> > > Sent: 14 October 2004 00:23
> > > To: statalist@hsphsun2.harvard.edu
> > > 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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