Arbeitspapier
Smoothed L-estimation of regression function
The Nadaraya-Watson estimator of regression is known to be highly sensitive to the presence of outliers in the sample. A possible way of robustication consists in using local L-estimates of regression. Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function. We show that this smoothed L-estimation approach provides computational as well as statistical finite sample improvements. The asymptotic distribution of the estimator is derived under mild Ø-mixing conditions.
- Language
-
Englisch
- Bibliographic citation
-
Series: SFB 373 Discussion Paper ; No. 2002,88
- Classification
-
Wirtschaft
- Subject
-
nonparametric regression
L-estimation
smoothed cumulative distribution function
- Event
-
Geistige Schöpfung
- (who)
-
Tamine, Julien
Čížek, Pavel
Härdle, Wolfgang
- Event
-
Veröffentlichung
- (who)
-
Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
- (where)
-
Berlin
- (when)
-
2002
- Handle
- URN
-
urn:nbn:de:kobv:11-10049692
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
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
- Tamine, Julien
- Čížek, Pavel
- Härdle, Wolfgang
- Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
Time of origin
- 2002
Other Objects (12)
Efficient estimation in single-index regression
Multivariate and semiparametric kernel regression
Adaptive copula estimation
Financial Applications ofClassification and Regression Trees
Asymptotic normality of parametric part in partial linear heteroscedastic regression models
Robust adaptive estimation of dimension reduction space
On adaptive estimation in partial linear models
On adaptive estimation in partial linear models
Discrete time option pricing with flexible volatility estimation
Semi-parametric estimation of generalized partially linear single-index models
Dynamics of state price densities
Dynamic nonparametric state price density estimation using constrained least squares and the bootstrap
Efficient estimation in single-index regression
Multivariate and semiparametric kernel regression
Adaptive copula estimation
Financial Applications ofClassification and Regression Trees
Asymptotic normality of parametric part in partial linear heteroscedastic regression models
Robust adaptive estimation of dimension reduction space
On adaptive estimation in partial linear models
On adaptive estimation in partial linear models
Discrete time option pricing with flexible volatility estimation
Semi-parametric estimation of generalized partially linear single-index models
Dynamics of state price densities
Dynamic nonparametric state price density estimation using constrained least squares and the bootstrap
Efficient estimation in single-index regression
Multivariate and semiparametric kernel regression
Adaptive copula estimation
Financial Applications ofClassification and Regression Trees
Asymptotic normality of parametric part in partial linear heteroscedastic regression models
Robust adaptive estimation of dimension reduction space
On adaptive estimation in partial linear models
On adaptive estimation in partial linear models
Discrete time option pricing with flexible volatility estimation
Semi-parametric estimation of generalized partially linear single-index models
Dynamics of state price densities