Abstract
This paper deals with the convergence in Mallows metric for classical multivariate kernel distribution function estimators. We prove the convergence in Mallows metric of a locally orientated kernel smooth estimator belonging to the class of sample smoothing estimators. The consistency follows for the smoothed bootstrap for regular functions of the marginal means. Two simple simulation studies show how the smoothed versions of the bootstrap give better results than the classical technique.
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