st: Robust variances

[Constantine Daskalakis <c_daskalakis@entwhistle.jci.tju.edu>]

At 04:48 PM 5/23/03, Mark Schaffer wrote:
> Hi everybody.  With respect to clustering,
>
> Personally, I think this problem is VERY easy to stumble into [decoded =
> I've done it myself] and could do with much more highlighting in the
> manuals, and in the on-line help and error messages.
>
> --Mark
I am confused.

Consider the simplest case where I compute a robust variance from all the 
data (ie, a single cluster). Are you saying that I get information 
equivalent to an observation of 1? That is certainly not the case. Only if 
the actual correlation is 1, I will get an "effective" N of 1. If the 
actual correlation is 0, I will get an "effective" N similar to my original 
observations. In this simple case, are you then saying that the robust 
variance is nonsense? The Liang and Zeger GEE approach does exactly that 
and it's been shown to be consistent in lots of situations, so your point 
must be different.

Maybe you're arguing that, with a single cluster, the robust variance is 
fine, but when you sum across clusters, then you have to have a "large 
number" of clusters?

Any comments/direction from the good Stata people on this?

cd





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________________________________________________________________

Constantine Daskalakis, ScD
Assistant Professor,
Biostatistics Section, Thomas Jefferson University,
125 S. 9th St. #402, Philadelphia, PA 19107
   Tel: 215-955-5695
   Fax: 215-503-3804
   Email: c_daskalakis@mail.jci.tju.edu
   Webpage: http://www.kcc.tju.edu/Science/SharedFacilities/Biostatistics

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