new tech report available

[tibs@utstat.toronto.edu]

          Model search and inference by bootstrap ``bumping''

               Robert Tibshirani and Keith Knight

                   University of Toronto

We propose a bootstrap-based method  for searching through a space of
models.  The technique is well suited to complex, adaptively fitted
models:  it provides a convenient method for finding better local
minima, for resistant fitting, and for optimization under constraints.
Applications to regression, classification and density estimation are
described.  The collection of models can also be used to form a
confidence set for the true underlying model, using a generalization of
Efron's percentile interval.  We also provide results on the
asymptotic behaviour of  bumping estimates.


Available at

http://utstat.toronto.edu/reports/tibs

or

ftp: utstat.toronto.edu  in  pub/tibs/bumping.ps
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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
tibs at utstat.toronto.edu. ftp: //utstat.toronto.edu/pub/tibs
http://www.utstat.toronto.edu/~tibs/home.html
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