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.her...@unibas.ch
http://www.psycho.unibas.ch/herzog/
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