Boosting Estimation of RBF Neural Networks for Dependent Data

George Kapetanios () and Andrew Peter Blake ()
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George Kapetanios: Queen Mary, University of London, http://www.econ.qmul.ac.uk/staff/kapetanios/

No 588, Working Papers from Queen Mary, University of London, Department of Economics

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.

Keywords: Neural Networks; Boosting (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets and nep-soc
Date: 2007-03

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Persistent link: http://EconPapers.repec.org/RePEc:qmw:qmwecw:wp588

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