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st: Robust error variance and model selection


From   "Romain CASEY" <romain.casey@gmail.com>
To   Statalist <statalist@hsphsun2.harvard.edu>
Subject   st: Robust error variance and model selection
Date   Thu, 7 Feb 2008 13:50:27 +0100

Dear all,
I want to estimate a relative risk in the same way that it is presented here:
<http://www.ats.ucla.edu/stat/stata/faq/relative_risk.htm>. When I
tried a log-binomial regression, some convergence problems emerged so
I prefer to use a Poisson regression with a robust error variance.
With this kind of estimation of the variance, no likelihood is
calculated but only pseudo-likelihood. So, I don't know which methods
to use for making a model selection : LRT, AIC, etc. are not usable.
Moreover, how to test goodness-of-fit of the different models ? If I
use the command "poisson y x, irr robust" instead of the command "glm
y x, fam(poisson) link(log) robust", I can use the "estat gof,
pearson" command but I'm note sure that this test can be apply when a
robust variance is used.

Thanks a lot for your help.
Kind regards,

Romain Casey
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