This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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490
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Type:
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Invited
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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Abstract - #306274 |
Title:
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Discovering Regression Structure with a Bayesian Ensemble
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Author(s):
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Edward I. George*+
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Companies:
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University of Pennsylvania
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Address:
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3730 Walnut St, Philadelphia, PA, 19104,
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Keywords:
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boosting ;
model building ;
nonparametric regression ;
random forests ;
trees ;
variable selection
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Abstract:
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A Bayesian ensemble approach can be used to discover and learn about an unknown regression relationship between a variable of interest y and a vector of predictors x. The basic idea is to model the conditional distribution of y given x by a sum of random basis elements plus a flexible noise distribution. BART, a special case which uses random regression trees as basis functions, can automatically produce the predictive distribution of y at any x (in or out of sample). It can do this for nonlinear relationships, even when hidden within a large number of irrelevant predictors, and by constraining the number of trees to create a bottleneck effect, it can be used for model free variable selection. Ultimately, the many features of such an approach may be seen as a valuable first step towards model building for high dimensional data. This is joint work with H. Chipman and R. McCulloch.
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Authors who are presenting talks have a * after their name.
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