M robustified additive nonparametric regression

Julien Tamine, Wolfgang Härdle and Lijian Yang

No 2002,69, SFB 373 Discussion Papers from Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes

Abstract: Additive modelling has been widely used in nonparametric regression to circumvent the curse of dimensionality, by reducing the problem of estimating a multivariate regression function to the estimation of its univariate components. Estimation of these univariate functions, however, can suffer inaccuracy if the data set is contaminated with extreme observations. As detection and removal of outliers in high dimension is much more difficult than in one dimension, we propose an M type marginal integration estimator that automatically corrects the extreme influence of outliers. We establish the robustness and obtain the asymptotic distribution of the M estimator through the functional approach. As a consequence, our results are valid for ,ß-mixing samples under mild constraints. Monte Carlo study confirm our theoretical results.

Keywords: Frechet differential; kernel estimator; marginal integration; M estimator; outliers; robustness (search for similar items in EconPapers)
Date: 2002
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/65354/1/727064290.pdf (application/pdf)

Related works:
Working Paper: R robustified additive nonparametric regression (2002)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb373:200269

Access Statistics for this paper

More papers in SFB 373 Discussion Papers from Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics (econstor@zbw-workspace.eu).