Multiple Kernel Procedure: an Asymptotic Support

Philippe Vieu

Scandinavian Journal of Statistics, 1999, vol. 26, issue 1, 61-72

Abstract: ABSTRACT. This paper deals with kernel non‐parametric estimation. The multiple kernel method, as proposed by Berlinet (1993), consists in choosing both the smoothing parameter and the order of the kernel function. In this paper we follow this general idea, and the selection is carried out by a combination of plug‐in and cross‐validation techniques. In a first attempt we give an asymptotic optimality theorem which is stated in a general unifying setting that includes many curve estimation problems. Then, as an illustration, it will be seen how this behaves in both special cases of kernel density and kernel regression estimation.

Date: 1999
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