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
Publisher:	Berlin : Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
Subject:	robustness \| nonparametric regression \| influence function \| M-estimator \| L-estimator \| R-estimator \| Von-mises statistical functional generalized Delta-theorem

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Series:	SFB 373 Discussion Paper ; 2002,62
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	727037854 [GVK] hdl:10419/65369 [Handle] RePEc:zbw:sfb373:200262 [RePEc]
Classification:	C13 - Estimation ; C14 - Semiparametric and Nonparametric Methods ; C15 - Statistical Simulation Methods; Monte Carlo Methods
Source:	EconStor

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010310591