Multiplicative Bias Corrected Nonparametric Smoothers with Application to Nuclear Energy Spectrum Estimation

Nicolas Hengartner (1) , Eric Matzner-Løber (2) , Laurent Rouviere (2, 3) , Thomas Burr (1)

1 LANL - Theorical Division
2 IRMAR - Institut de Recherche Mathématique de Rennes
3 CREST - Centre de Recherche en Économie et Statistique

The paper presents a multiplicative bias reduction estimator for nonparametric regression. The approach consists to apply a multiplicative bias correction to an oversmooth pilot estimator. We study the asymptotic properties of the resulting estimate and prove that this estimate has zero asymptotic bias and the same asymptotic variance as the local linear estimate. Simulations show that our asymptotic results are available for small sample sizes. We also illustrate the benefit of this new method on nuclear energy spectrum estimation.

Mots clés: Nonparametric regression; bias reduction; local linear estimate

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Nicolas Hengartner, Eric Matzner-Løber, Laurent Rouviere, Thomas Burr. Multiplicative Bias Corrected Nonparametric Smoothers with Application to Nuclear Energy Spectrum Estimation. 2009. ⟨hal-00408696v1⟩