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Tamine, Julien
Čížek, Pavel
Härdle, Wolfgang
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SFB 373 Discussion Paper 2002,88
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.
nonparametric regression
smoothed cumulative distribution function
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Working Paper
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