robust model selection criteria
On Fri, 29 Apr 2005, Berton Gunter wrote:
-- 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)
More fundamentally, `AIC' is about maximum-likelihood fitting of true models. Now rlm does usually correspond to ML fitting of a non-normal linear model, so it would be possible to compute a likelihood and hence AIC. The point however is that the model is assumed to be false. There are AIC-like criteria for that situation, but they are essentially impossible to compute accurately as they depend on fine details of the unknown true error distribution (and still assume a linear model).
-- 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
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