Local CQR Smoothing: An Efficient and Safe Alternative to Local Polynomial Regression.

ABSTRACT: Local polynomial regression is a useful nonparametric regression tool to explore fine data structures and has been widely used in practice. In this paper, we propose a new nonparametric regression technique called local composite-quantile-regression (CQR) smoothing in order to further improve local polynomial regression. Sampling properties of the proposed estimation procedure are studied. We derive the asymptotic bias, variance and normality of the proposed estimate. Asymptotic relative efficiency of the proposed estimate with respect to the local polynomial regression is investigated. It is shown that the proposed estimate can be much more efficient than the local polynomial regression estimate for various non-normal errors, while being almost as efficient as the local polynomial regress

SUBMITTER: Kai B 

PROVIDER: S-EPMC2958780 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature