Smoothed L-estimation of regression function - Julien Tamine; Pavel Čížek; Wolfgang Härdle (Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes, 2002)
The Nadaraya-Watson estimator of regression is known to be highly sensitive to the presence of outliers in the sample. A possible way of robustication consists in using local L-estimates of regression. Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function. We show that this smoothed L-estimation approach provides computational as well as statistical finite sample improvements. The asymptotic distribution of the estimator is derived under mild Ø-mixing conditions.
urn:nbn:de:kobv:11-10049692
Keywords: 330; nonparametric regression; L-estimation; smoothed cumulative distribution function