Smoothed influence function: Another view at robust nonparametric regression

In this work, we introduce a smoothed influence function that constitute a theoretical tool for studying the outliers robustness properties of a large class of nonparametric estimators. With this tool, we first show the nonrobustness of the Nadaraya-Watson estimator of regression. Then we show that the M, the L and the R-estimators of the regression achieve robustness (when estimated by kernel). Our results are illustrated performing Monte-Carlo simulation.

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Year of publication:	2001
Authors:	Tamine, Julien
Institutions:	Sonderforschungsbereich 373, Quantifikation und Simulation ökonomischer Prozesse, Wirtschaftswissenschaftliche Fakultät
Subject:	robustness \| nonparametric regression \| influence function \| M-estimator \| L-estimator \| R-estimator \| Von-mises statistical functional generalized Delta-theorem

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Extent:	application/pdf
Series:	SFB 373 Discussion Papers.
Type of publication:	Book / Working Paper
Notes:	Number 2002,62
Classification:	C13 - Estimation ; C14 - Semiparametric and Nonparametric Methods ; C15 - Statistical Simulation Methods; Monte Carlo Methods
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010956562