[R] robust model selection criteria

[Berton Gunter]

-- Bert Gunter
Genentech Non-Clinical Statistics
South San Francisco, CA
 
"The business of the statistician is to catalyze the scientific learning
process."  - George E. P. Box
 
 

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of 
> Carsten.Colombier at efv.admin.ch
> Sent: Friday, April 29, 2005 9:26 AM
> To: r-help at stat.math.ethz.ch
> Subject: [R] robust model selection criteria
> 
> Dear R-help-team,
> 
> do you know if there is a package for R available that 
> contains a function,
> which calculates a robust model selection criterium like 

> robust AIC and has
> a robust selection function like "step" for lm-objects, for 
> an  rlm-object.
> Unfortunately, functions like "step" or "stepAIC" cannot be applied to
> rlm-objects. Moreover, these functions do not use  robust AIC.
> 

??? How could this be meaningful? The robust "likelihood" need not increase
as more parameters are added because of the robust reweighting (points would
be downweighted differently in the different models). How do you account for
the number of "parameters" in a robust model given that it is in essence
nonlinear?

(This comment subject to correction/expansion by wiser heads than me)

-- Bert Gunter
Genentech Non-Clinical Statistics
South San Francisco, CA
 
"The business of the statistician is to catalyze the scientific learning
process."  - George E. P. Box