Michal Figurski wrote:
Thank you all for your words of wisdom.
I start getting into what you mean by bootstrap. Not surprisingly, it
seems to be something else than I do. The bootstrap is a tool, and I
would rather compare it to a hammer than to a gun. People say that
hammer is for driving nails. This situation is as if I planned to use it
to break rocks.
The key point is that I don't really care about the bias or variance of
the mean in the model. These things are useful for statisticians;
regular people (like me, also a chemist) do not understand them and have
no use for them (well, now I somewhat understand). My goal is very
practical: I need an equation that can predict patient's outcome, based
on some data, with maximum reliability and accuracy.
I have found from the mentioned paper (and from my own experience) that
re-sampling and running the regression on re-sampled dataset multiple
times does improve predictions. You have a proof of that in that paper,
page 1502, and to me it is rather a stunning proof: compare 56% to 82%
of correctly predicted values (correct means within 15% of original value).
Michal I think you are misinterpreting that paragraph, although it's
hard to know exactly what they mean there. I think it is a comparison
of a naive stepwise regression approach to a more pre-specified approach
that selects the model using a more unbiased criterion than that used by
stepwise selection. Resampling can be useful for selecting the form of
the model in some cases. But your original post dealt with resampling
from a single pre-specified model which is not what the authors used.
(remainder omitted)
Frank
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