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Tech report available:


The out-of-bootstrap method for model averaging and selection


  ...enjoying the Bayesian omelette without making a mess in the kitchen


 J. Sunil Rao and   Robert Tibshirani

We propose a bootstrap-based method for model averaging and selection
that focuses on training points that are left out of individual
bootstrap samples.  This information can be used to estimate optimal
weighting factors for combining estimates from different bootstrap
samples, and also for finding the best subsets the linear model
setting. These proposals provide alternatives to Bayesian approaches to
model averaging and selection, requiring less computation and fewer
subjective choices.

Comments welcome


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

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

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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@utstat.toronto.edu. ftp: //utstat.toronto.edu/pub/tibs
http://www.utstat.toronto.edu/~tibs
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