Abstract
Keeping the estimator of regression parameters stable and achieving variable selection of the model, we focus on Breiman's non-negative garrote (NNG) as a shrinkage estimator and propose two boosting methods of the NNG. One is the method that NNG runs with multi-fold cross validation in each boosting iterations, and the other is the method that NNG runs without cross validation in each boosting iterations. Then, we evaluated the performance of the proposed methods through small scale simulation. As a result, the proposed methods kept the estimator more stable than the naive NNG. So we have showed boosting method is also useful for the estimation of regression parameters.
