Spring 2009 Thursday, March 5   Speaker: Hui Zou School of Statistics The University of Minnesota Title: "Local CQR Smoothing" Abstract: Local polynomial regression is a useful nonparametric regression tool to explore fine data structures and has been widely used in practice. In this talk we present a new nonparametric regression technique called local 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 regression estimate for normal errors. Simulation is conducted to examine the performance of the proposed estimates. The simulation results are consistent with our theoretical findings. A real data example is used to illustrate the proposed method. This talk is based on a joint paper with Kai Bo and Runze Li.

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