Bias reduction by projection on parametric models in Hilbertian nonparametric regression

초록

In this paper we discuss a general way of improving the bias properties of nonparametric kernel regression estimators. The procedure involves choosing a parametric model and constructing a semiparametric estimator that consists of a parametric component and a nonparametric adjustment, where the parametric component is picked from the parametric model in such a way that the resulting estimator has the best bias performance. We study the method for response variables taking values in a general Hilbert space and for local linear smoother. We show that the procedure always improves the bias of the local linear estimator regardless of the choice of parametric model. We also illustrate the method via a real data example where the response variable is a random density.

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