new paper on model selection

[tibs@utstat.toronto.edu]

The covariance inflation criterion for adaptive model selection


                Rob Tibshirani  and  Keith Knight

                       Univ of Toronto


We propose a new criterion for model selection in  prediction
problems.  The covariance inflation criterion  adjusts the training
error by the average covariance of the predictions and responses, when
the prediction rule is applied to permuted versions of the dataset.
This criterion can be applied to general prediction problems (for
example regression or classification), and to general prediction rules
(for example stepwise regression, tree-based models and neural nets).
As a byproduct we  obtain a measure of the effective number of
parameters used by an adaptive procedure.  We relate the covariance
inflation criterion to other model selection procedures and illustrate
its use in some regression and classification problems.  We also
revisit the conditional bootstrap approach to model selection.


Available at 

http://utstat.toronto.edu/tibs/research.html

or 

ftp://utstat.toronto.edu/pub/tibs/cic.ps 

Comments welcome!

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Rob Tibshirani, Dept of Preventive Med & Biostats, and Dept of Statistics
Univ of Toronto, Toronto, Canada M5S 1A8.
Phone: 416-978-4642 (PMB), 416-978-0673 (stats). FAX: 416 978-8299
computer fax  416-978-1525 (please call or email me to inform)
tibs at utstat.toronto.edu. ftp: //utstat.toronto.edu/pub/tibs
http://www.utstat.toronto.edu/~tibs
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