Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22638
Authors: 
Kiwitt, Sebastian
Nagel, Eva-Renate
Neumeyer, Natalie
Year of Publication: 
2005
Series/Report no.: 
Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2005,45
Abstract: 
The aim of this paper is to show that existing estimators for the error distribution in nonparametric regression models can be improved when additional information about the distribution is included by the empirical likelihood method. The weak convergence of the resulting new estimator to a Gaussian process is shown and the performance is investigated by comparison of asymptotic mean squared errors and by means of a simulation study. As a by-product of our proofs we obtain stochastic expansions for smooth linear estimators based on residuals from the nonparametric regression model.
Subjects: 
empirical distribution function
empirical likelihood
error distribution
estimating function
nonparametric regression
Owen estimator
Document Type: 
Working Paper

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