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On bagging and nonlinear estimation

Friedman, Jerome H.; Hall, Peter


We propose an elementary model for the way in which stochastic perturbations of a statistical objective function, such as a negative log-likelihood, produce excessive nonlinear variation of the resulting estimator. Theory for the model is transparently simple, and is used to provide new insight into the main factors that affect performance of bagging. In particular, it is shown that if the perturbations are sufficiently symmetric then bagging will not significantly increase bias; and if the...[Show more]

CollectionsANU Research Publications
Date published: 2007
Type: Journal article
Source: Journal of Statistical Planning and Inference
DOI: 10.1016/j.jspi.2006.06.002


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