A Fully Automated Bandwidth Selection

This article describes a fully automated bandwidth selection method for additive models that is applicable to the widely used back tting algorithm of Buja, Hastie and Tibshirani (1989) and does not rely on cross-validation. The proposed plug-in estimator is an extension of the local linear regression estimator of Ruppert, Sheather and Wand (1996) and is shown to achieve the same Op(n 2=7) relative convergence rate for bivariate additive models. If more than two covariates are present, theoretical justi cation of the method requires independence of the covariates, but simulation experiments show that in practice the method is very robust to violations of this assumption. The behavior of the method is demonstrated on a real dataset.