Tal Galili wrote:
Hello Frank,
Thank you for the extension and remarks.
The basic weakness of stepwise regression VS going through all-subsets
is very much agreed upon. Although from what I gather there is one case
where all subsets will be a problem to implement, that is for very LARGE
datasets - especially in the sense of a lot of explanatory variables,
and also with regards to cases where we have more explanatory variables
then data points.
In such cases I wonder if using stepwise regression could be found to be
more realistic to implement then all subsets checks.
Then again, I imagine (although not from real experience) that shrinkage
methods (used with LARS) could be practical in those cases too.
I am looking forward to meeting you on Tuesday and taking your first
tutorial of the day,
I look forward to seeing you in Rennes.
With regard,
Tal
All subsets regression is an especially bad form of stepwise regression.
It has terrible operating characteristics.
Cheers,
Frank
On Sat, Jul 4, 2009 at 4:22 PM, Frank E Harrell Jr
<f.harr...@vanderbilt.edu <mailto:f.harr...@vanderbilt.edu>> wrote:
sed for one variable at a time variable selection. AIC is just a
restatement of the P-value, and as such, doesn't solve the severe
problems with stepwise v
--
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Tal Galili
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Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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