title: Bootstrapping neural networks.
abstract: 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 boot-strap, 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.
authors.affiliation: Department of Mathematics, University of Kaiserslautern, 67653 Kaiserslautern, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Franke
authors.forename: M H
authors.initials: MH
authors.lastname: Neumann
mesh_terms: D016477:Artifacts; D003198:Computer Simulation; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011336:Probability; D012044:Regression Analysis