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.

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Year of publication:	2007
Authors:	Kapetanios, George ; Blake, Andrew P.
Publisher:	London : Queen Mary University of London, Department of Economics
Subject:	Neuronale Netze \| Nichtparametrisches Verfahren \| SchÃƒÂ¤tztheorie \| Neural Networks, Boosting

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Series:	Working Paper ; 588
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	525991751 [GVK] hdl:10419/62937 [Handle]
Classification:	C12 - Hypothesis Testing ; C13 - Estimation ; C22 - Time-Series Models
Source:	EconStor

Persistent link: https://www.econbiz.de/10010284226