Abstract
In this paper we investigate the usage of the AR-Sieve bootstrap method to estimate the standard error of the sampling distribution of the neural network predictive values in a regression model with dependent errors. The performance of the proposed approach is evaluated by a Monte Carlo experiment where it is also compared with the classical residual bootstrap scheme.
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© 2002 Springer-Verlag London Limited
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Giordano, F., La Rocca, M., Perna, C. (2002). Standard Error Estimation in Neural Network Regression Models: the AR-Sieve Bootstrap Approach. In: Tagliaferri, R., Marinaro, M. (eds) Neural Nets WIRN Vietri-01. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0219-9_21
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DOI: https://doi.org/10.1007/978-1-4471-0219-9_21
Publisher Name: Springer, London
Print ISBN: 978-1-85233-505-2
Online ISBN: 978-1-4471-0219-9
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