Bootstrapping Neural Networks

Authors:
Jürgen Franke;Michael H. Neumann
Affiliations:
Department of Mathematics, University of Kaiserslautern, 67653 Kaiserslautern, Germany;Department of Economics, Humboldt University of Berlin, 13591 Berlin, Germany
Venue:
Neural Computation
Year:
2000

Citing 3
Cited 5

Multilayer feedforward networks are universal approximators

Neural Networks
Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings

Neural Networks
Bootstrapping confidence intervals for clinical input variable effects in a network trained to identify the presence of acute myocardial infarction

Neural Computation

Financial Time Series Modelling Using Neural Networks: An Assessment of the Utility of a Stacking Methodology

AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
On the Consistency of the Blocked Neural Network Estimator in Time Series Analysis

Neural Computation
Diversity of ability and cognitive style for group decision processes

Information Sciences: an International Journal
Selective combination of multiple neural networks for improving model prediction in nonlinear systems modelling through forward selection and backward elimination

Neurocomputing
On the symbolic analysis of market indicators with the dynamic programming approach

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

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.