Please use this identifier to cite or link to this item:
Tamine, Julien
Härdle, Wolfgang
Yang, Lijian
Year of Publication: 
Series/Report no.: 
Discussion Papers, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes 2002,69
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.
Frechet differential
kernel estimator
marginal integration
M estimator
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
245.47 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.