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Activity Number: 360
Type: Invited
Date/Time: Wednesday, August 9, 2006 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #307212
Title: Bias and Variance of Bagging Based on Subsampling with and without Replacement
Author(s): Andreas Buja*+
Companies: University of Pennsylvania
Address: The Wharton School, Philadelphia, PA, 19104-6340,
Keywords:
Abstract:

Bagging is a device intended to reduce the prediction error of learning algorithms by reducing their variance. In its original form, bagging draws bootstrap samples from the training sample, applies the learning algorithm to each bootstrap sample, and averages the resulting prediction rules. Variants of bagging are obtained by letting the resample size, M, be different from the sample size, N, where both MN are possible. Still, other variants are obtained when "resampling with" is replaced with "resampling without," in which case one needs M
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