288 pp. per issue
6 x 9, illustrated
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Neural Computation

August 1, 2000, Vol. 12, No. 8, Pages 1929-1949
(doi: 10.1162/089976600300015204)
© 2000 Massachusetts Institute of Technology
Bootstrapping Neural Networks
Article PDF (263.45 KB)

Knowledge about the distribution of a statistical estimator is important for various purposes, such as the construction of confidence intervals for model parameters or the determination of critical values of tests. A widely used method to estimate this distribution is the so-called bootstrap, which is based on an imitation of the probabilistic structure of the data-generating process on the basis of the information provided by a given set of random observations. In this article we investigate this classical method in the context of artificial neural networks used for estimating a mapping from input to output space. We establish consistency results for bootstrap estimates of the distribution of parameter estimates.