Arbeitspapier
Empirical likelihood estimators for the error distribution in nonparametric regression models
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.
- Language
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Englisch
- Bibliographic citation
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Series: Technical Report ; No. 2005,45
- Subject
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empirical distribution function
empirical likelihood
error distribution
estimating function
nonparametric regression
Owen estimator
- Event
-
Geistige Schöpfung
- (who)
-
Kiwitt, Sebastian
Nagel, Eva-Renate
Neumeyer, Natalie
- Event
-
Veröffentlichung
- (who)
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Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (where)
-
Dortmund
- (when)
-
2005
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Kiwitt, Sebastian
- Nagel, Eva-Renate
- Neumeyer, Natalie
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Time of origin
- 2005
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