I am interested in comparing the fit of robust (i.e., S and MM) and
non-robust (i.e., OLS) estimators when applied to a particular data set.
The paper entitled "A comparison of robust versions of the AIC based on M, S
and MM-estimators" (available at:
http://ideas.repec.org/p/ner/leuven/urnhdl123456789-274771.html) presents
formulas for robust Akaike information criteria (AIC) for the M, S, and MM
estimators.  Unfortunately, these omit the term {n + n*ln(2*pi)} that is
included in the standard AIC formula used by R's AIC function.  Would it be
appropriate to either (1) include the term in the robust estimator formulas
to make them comparable with the standard AIC formula or (2) omit the term
from the standard AIC formula to make it comparable with the robust
estimator formulas?  All of the models I am comparing include the same
independent and dependent variables.  Only the estimator is being varied.
Also, is anyone aware of an AIC analog that would be applicable to least
trimmed squares estimation?      

 

-- 

Jim

 

 

James W. Shaw, Ph.D., Pharm.D., M.P.H.

Assistant Professor

Department of Pharmacy Administration 

College of Pharmacy

University of Illinois at Chicago

833 South Wood Street, M/C 871, Room 266

Chicago, IL 60612

Tel.: 312-355-5666

Fax: 312-996-0868

Mobile Tel.: 215-852-3045

 


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