Smoothed L-estimation of regression function

Ci­zek, P., J. Tamine and W. Härdle
Authors registered in the RePEc Author Service: Pavel Cizek

Computational Statistics & Data Analysis, 2008, vol. 52, issue 12, 5154-5162

Abstract: The Nadaraya-Watson nonparametric estimator of regression is known to be highly sensitive to the presence of outliers in data. This sensitivity can be reduced, for example, by 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. The asymptotic distribution of the proposed estimator is derived under mild [beta]-mixing conditions, and additionally, we show that the smoothed L-estimation approach provides computational as well as statistical finite-sample improvements. Finally, the proposed method is applied to the modelling of implied volatility.

Date: 2008
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Citations: View citations in EconPapers (1)

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