On 05/19/2010 01:39 PM, Ben Bolker wrote:
Frank E Harrell Jr<f.harrell<at>  Vanderbilt.Edu>  writes:


Please read the large number of notes in the e-mail archive about the
invalidity of such modeling procedures.

Frank


   I'm curious: do you have an objection to multi-model averaging
a la Burnham, Anderson, and White (as implemented in the MuMIn
package)?  i.e., *not* just picking the
best model, and *not* trying to interpret statistical significance
of particular coefficients, but trying to maximize predictive
capability by computing the AIC values of many candidate models
and weighting predictions accordingly (and incorporating among-model variation
when computing prediction uncertainty)?  (I would look for the
answer in your book, but I have lost my copy by loaning it out
&  haven't got a new one yet ...)

Hi Ben,

I think that model averaging (e.g., Bayesian model averaging) works extremely well. But if you are staying within one model family, it is a lot more work than the equally excellent penalized maximum likelihood estimation of a single (big) model. The latter uses more standard tools and can isolate the effect of one variable and result in ordinary model graphics.

I haven't seen a variable selection method that works well without penalization (shrinkage).

Frank

--
Frank E Harrell Jr   Professor and Chairman        School of Medicine
                     Department of Biostatistics   Vanderbilt University

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