Re: is AIC always 100% in evaluating a model?

> 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.
>
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>
> I am looking forward to meeting you on Tuesday and taking your first
> tutorial of the day,

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> On Sat, Jul 4, 2009 at 4:22 PM, Frank E Harrell Jr
> <f.harrell@... <mailto:f.harrell@...>> 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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> --
> ----------------------------------------------
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>
> My contact information:
> Tal Galili
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>