This study considers the theoretical bootstrap “coupling” techniques for nonparametric robust smoothers and quantile regression, and we verify the bootstrap improvement. To handle the curse of dimensionality, a variant of “coupling” bootstrap techniques is developed for additive models...

We consider theoretical bootstrap "coupling" techniques for nonparametric robust smoothers and quantile regression, and verify the bootstrap improvement. To cope with curse of dimensionality, a variant of "coupling" bootstrap techniques are developed for additive models with both symmetric error...

Classification models are very sensitive to data uncertainty, and finding robust classifiers that are less sensitive to data uncertainty has raised great interest in the machine learning literature. This paper aims to construct robust support vector machine classifiers under feature data...