Abstract
In this paper we consider the problem of variables selection in a non linear regression model with dependent errors. In this framework, we discuss the use of some measures for the variables relevance to the neural network model and we propose the use of the moving block bootstrap technique to estimate the variability of these measures. The performance of the procedure is evaluated by a small Monte Carlo experiment which shows how the proposed approach determines a correct ranking among relevant and irrelevant variables.
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© 2004 Springer-Verlag Berlin Heidelberg
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Giordano, F., Rocca, M.L., Perna, C. (2004). Bootstrap Variables Selection in Neural Network Regression Models. In: Bock, HH., Chiodi, M., Mineo, A. (eds) Advances in Multivariate Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17111-6_9
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DOI: https://doi.org/10.1007/978-3-642-17111-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20889-1
Online ISBN: 978-3-642-17111-6
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