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Title:Boosting estimation of RBF neural networks for dependent data PDF Logo
Authors:Kapetanios, George
Blake, Andrew P.
Issue Date:2007
Series/Report no.:Working Paper, Department of Economics, Queen Mary, University of London 588
Abstract: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.
Subjects:Neural Networks, Boosting
JEL:C12
C13
C22
Document Type:Working Paper
Appears in Collections:Working Paper Series, School of Economics and Finance, Queen Mary, University of London

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