[RsR] Robust (approximated) Bayesian statistics: BIC of robust methods?
Elvezio Ronchetti
E|vez|o@Ronchett| @end|ng |rom un|ge@ch
Wed Sep 16 12:31:33 CEST 2009
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
tel +41 22 379 8131
tel (secr) +41 22 379 8229
Fax +41 22 379 8299
http://www.unige.ch/ses/metri/ronchetti/
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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