Abstract
Boosting is a bias reduction technique while bagging is a variance reduction method. These two methods aim at reducing the asymptotic mean integrated square error (AMISE). This study aims to show that bagging is a boosting algorithm in kernel density estimation since both techniques use large smoothing parameter(s). This relationship was verified by real and simulated data
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