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RE: st: RE: negative binomial


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: negative binomial
Date   Thu, 6 Feb 2003 19:14:40 -0000

grsanta@unict.it
 
> many thanks for answering. I am not sure I understood what 
> you said. I tell you 
> what I have done:
> 1. I have run a poisson regression model where I estimated 
> a count data 
> dependent variable as a function of 13 variables calculated 
> across 90 
> observations + a costant, using the "poisson" command.
> 2. by using the "poisgof" command I then evaluated the 
> goodness of fit, which 
> gives me a chi2 prob = 0.9.
> 3. I have also run a negative binomial regression model on 
> the same data, by 
> using the command "nbreg" as well as the command "nbreg 
> [var], dispersion 
> (constant)". In the first case, the alfa was not 
> significant (chi2=0.5). In the 
> second case, the alfa was also not significant (although 
> the chi2 prob=0.25).
> 4.I have then use the command "summarize [var], detail" in 
> order to have the 
> mean and variance of the dependent variable. The result is 
> that the difference 
> between the two measures is about 0.40.
> 5. Following a procedure suggested in the FAQs STATA web 
> page, I have then used 
> the "nbvarg" command in order to estimate the theoretical 
> probability for the 
> poisson and negative binomial distributions as well as for 
> the observed. But, 
> if I have understood you correctly, the two theoretical 
> distributions cannot be 
> compared.
> QUESTIONS
> 1. Do you think I can adopt the poisson regression model 
> given these results? 

Sorry, I don't think one should presume to give research 
advice like this.  I'm not an expert on these models 
and even I were I don't have a sight of your data 
or a feeling for whatever science is involved. 

> 2. What do you mean by estimating the nbreg with or without 
> covariates? 

Covariates here = explanatory, predictor, "independent" 
variables. If I go 

. nbreg varname 

I am fitting a model without covariates, i.e. a 
unconditional negative binomial distribuion. It might 
not be parameterised as you would wish, but that 
is a different matter. -nbvargr- (and also -nbfit- 
on SSC) are different ways of approaching it. 

> 3. What do you mean by fitting the poisson with one 
> parameter and fitting the 
> negative binomial with two?

In the absence of covariates, the Poisson 
"model" has one parameter -- the mean response, say 
-- and the negative binomial has two. 
See any book on probability distributions for more 
detail. 

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