Bootstrapping neural networks

• Knowledge about the distribution of a statistical estimator is important for various purposes like, for example, the construction of confidence intervals for model parameters or the determiation 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 paper 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.

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Verfasserangaben: Jürgen Franke, Michael Neumann urn:nbn:de:hbz:386-kluedo-4804 Report in Wirtschaftsmathematik (WIMA Report) (38) Preprint Englisch 1998 1998 Technische Universität Kaiserslautern 03.04.2000 Fachbereich Mathematik 5 Naturwissenschaften und Mathematik / 510 Mathematik Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011

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