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.
Author: | Jürgen Franke, Michael Neumann |
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URN: | urn:nbn:de:hbz:386-kluedo-4804 |
Series (Serial Number): | Report in Wirtschaftsmathematik (WIMA Report) (38) |
Document Type: | Preprint |
Language of publication: | English |
Year of Completion: | 1998 |
Year of first Publication: | 1998 |
Publishing Institution: | Technische Universität Kaiserslautern |
Date of the Publication (Server): | 2000/04/03 |
Faculties / Organisational entities: | Kaiserslautern - Fachbereich Mathematik |
DDC-Cassification: | 5 Naturwissenschaften und Mathematik / 510 Mathematik |
Licence (German): | Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011 |