Authors: Kiwitt, Sebastian
Nagel, Eva­-Renate
Neumeyer, Natalie
Title: Empirical likelihood estimators for the error distribution in nonparametric regression models
Language (ISO): en
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 by­product of our proofs we obtain stochastic expansions for smooth linear estimators based on residuals from the nonparametric regression model. AMS Classification: 62G08, 62G05
Subject Headings: empirical distribution function
empirical likelihood
error distribution
estimating function
nonparametric regression
Owen estimator
URI: http://hdl.handle.net/2003/21671
http://dx.doi.org/10.17877/DE290R-14488
Issue Date: 2005-11-07T11:53:30Z
Appears in Collections:Sonderforschungsbereich (SFB) 475

Files in This Item:
File Description SizeFormat 
tr45-05.ps2.37 MBPostscriptView/Open
tr45-05.pdfDNB396.33 kBAdobe PDFView/Open


This item is protected by original copyright



All resources in the repository are protected by copyright.