On bagging and nonlinear estimation
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]
|Collections||ANU Research Publications|
|Source:||Journal of Statistical Planning and Inference|
|01_Friedman_On_bagging_and_nonlinear_2007.pdf||239.88 kB||Adobe PDF||Request a copy|
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