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Recursive bias estimation for multivariate regression smoothers

Pierre-André CornillonN. W. HengartnerE. Matzner-Løber — 2014

ESAIM: Probability and Statistics

This paper presents a practical and simple fully nonparametric multivariate smoothing procedure that adapts to the underlying smoothness of the true regression function. Our estimator is easily computed by successive application of existing base smoothers (without the need of selecting an optimal smoothing parameter), such as thin-plate spline or kernel smoothers. The resulting smoother has better out of sample predictive capabilities than the underlying base smoother, or competing structurally...

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