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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.
Year of publication:
2007-03
Authors: Kapetanios, George ; Blake, Andrew P.
Institutions: School of Economics and Finance, Queen Mary
Subject: Neural Networks | Boosting
Extent:
application/pdf
Series:
Type of publication: Book / Working Paper
Notes:
Number 588
Classification: C12 - Hypothesis Testing ; C13 - Estimation ; C22 - Time-Series Models
Source:
Persistent link: https://ebvufind01.dmz1.zbw.eu/10005106288
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