subset selection for logistic regression

To clarify Frank's remark ...

A prominent theme in statistical research over at least the last 25 years
(with roots that go back 50 or more, probably) has been the superiority of
"shrinkage" methods over variable selection. I also find it distressing that
these ideas have apparently not penetrated much (at all?) into the wider
scientific community (but I suppose I shouldn't be surprised -- most
scientists still do one factor at a time experiments 80 years after Fisher).
Specific incarnations can be found in anything Bayesian, mixed effects
models for repeated measures, ridge regression, and the R packages lars and
lasso, among others.

I would speculate that aside from the usual statistics/science cultural
issues, part of the reason for this is that the estimators don't generally
come with neat, classical inference procedures: like it or not, many
scientists have been conditioned by their Stat 101 courses to expect P
values, so in some sense, we are hoisted by our own petard.

Just my $.02 -- contrary(and more knowledgeable) opinions welcome.

-- Bert Gunter

subset selection for logistic regression

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