This paper addresses M-estimation of conditional mean functions when observations are missing at random. The usual approach of correcting for missing data, when the missing data mechanism is ignorable, is inverse probability weighting. An alternative semiparametric M-estimator which involves...

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...

Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions. We show that the recently proposed methods by Xia et al. (2002) can be made robust in such a way that preserves all advantages of the original...