[RsR] Robust (approximated) Bayesian statistics: BIC of robust methods?

[Elvezio Ronchetti]

Hi,

Check Machado(1993) Econometric Theory.
All the best.

Elvezio Ronchetti
Dept. of Econometrics
University of Geneva
Blv. Pont d'Arve 40
CH-1211 Geneva
SWITZERLAND

e-mail     Elvezio.Ronchetti using unige.ch
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Stefan Herzog wrote:
> Hi,
> 
> 
> I'm trying to find ressources on robust (approximated) Bayesian  
> statistics, but I'm not finding what I'm looking for; maybe you can  
> give me a hint where to look.
> 
> Basically I'm looking for a way to get a BIC (Bayesian information  
> criterion; Schwartz, 1978) for a model fit of robust methods. E.g. if  I 
> apply a robust regression (e.g., lmrob), is there a way to get  
> (something like) a BIC for the model? For some regression models in R  
> one can apply something like:
> 
> stepAIC (mymodel.glm, k=log(n))
> 
> Or one can calculate the BIC based on the SSEs (sum of squared  errors). 
> I somehow fear/feel that the SSE-approach cannot be directly  applied to 
> robust methods as they use different measures to obtain  their optimized 
> estimates (e.g. least trimmed squares regression  estimator).
> 
> Can you give me hint where to look or how to think about this issue?  
> Thanks!
> 
> Sorry if I'm asking a painfully obvious or wrong question.
> 
> 
> Best regards,
> 
> Stefan Herzog
> 
> 
> -------------------------------------------------------------
> Dr. Stefan Herzog, Research Scientist
> Center for Cognitive and Decision Sciences
> 
> Department of Psychology
> University of Basel
> Missionsstrasse 64A
> CH-4055 Basel
> Switzerland
> 
> Tel   +41 61 267 06 15
> Fax  +41 61 267 04 41
> stefan.herzog using unibas.ch
> http://www.psycho.unibas.ch/herzog/
> 
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