Arbeitspapier
Boosting estimation of RBF neural networks for dependent data
This paper develops theoretical results for the estimation of radial basis function neural network specifications, for dependent data, that do not require iterative estimation techniques. Use of the properties of regression based boosting algorithms is made. Both consistency and rate results are derived. An application to nonparametric specification testing illustrates the usefulness of the results.
- Language
-
Englisch
- Bibliographic citation
-
Series: Working Paper ; No. 588
- Classification
-
Wirtschaft
Hypothesis Testing: General
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Subject
-
Neural Networks, Boosting
Neuronale Netze
Nichtparametrisches Verfahren
Schätztheorie
- Event
-
Geistige Schöpfung
- (who)
-
Kapetanios, George
Blake, Andrew P.
- Event
-
Veröffentlichung
- (who)
-
Queen Mary University of London, Department of Economics
- (where)
-
London
- (when)
-
2007
- Handle
- Last update
-
10.03.2025, 11:41 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
- Kapetanios, George
- Blake, Andrew P.
- Queen Mary University of London, Department of Economics
Time of origin
- 2007
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