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[RsR] VIF for robust regression?

1 message · Olivier Renaud

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I don't know if there is work on a robust VIF. Here are my two cents:
- What type of variable do you have ? If some of them are discrete with 
very few different values, this may cause problem to robust covariance 
matrices estimation.
- Your approach to apply the "partialling out"  of the robust matrix, as 
in the OLS case,  might or might not be correct, I don't know.
- If you believe that 1/(1-R^2_i) is a good measure, then you might want 
to compute its direct robust equivalent.  The output of lmrob does not 
provide a R^2, but the output of lmRob does. We have recently published 
a paper however that shows that the robust R-squared provided by lmRob 
is biased, sometimes to a large extent. We provide a consistent and 
robust estimator of R-squared and a version adjusted for the sample 
size.  See my previous post for an example and the code at
https://stat.ethz.ch/pipermail/r-sig-robust/2010/000290.html

Olivier

ref: Renaud, O. & Victoria-Feser, M.-P. (2010). A robust coefficient of 
determination for regression. Journal of Statistical Planning and 
Inference, 140, 1852-1862. http://dx.doi.org/10.1016/j.jspi.2010.01.008
On 31/03/2011 17:35, Nicholas Lewin-Koh wrote: