A bias corrected nonparametric regression estimator

Abstract

In this article, we propose a new method of bias reduction in nonparametric regression estimation. The proposed new estimator has asymptotic bias order h4, where h is a smoothing parameter, in contrast to the the usual bias order h2 for the local linear regression. In addition, the proposed estimator has the same order of the asymptotic variance as the local liner regression. Our proposed method is closely related to the bias reduction method for kernel density estimate proposed by Chung and Lindsay (2011). However, our method is not a direct extension of their density estimate, but a totally new one based on the bias cancelation result of their proof.