Statistical inference, the bootstrap, and neural-network modeling with application to foreign exchange rates

IEEE Transactions on Neural Networks

Volumn 12, Issue 4, 2001, Pages 657-673

  White, Halbert ^a,b   Racine, Jeffrey ^c  

^a IEEE   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF SOUTH FLORIDA   (United States)

Author keywords

Bootstrap; Foreign exchange; Hypothesis testing; Neural networks; Prediction; Resampling

Indexed keywords

BOOTSTRAP; FOREIGN EXCHANGE RATES; HYPOTHESIS TESTING; STATISTICAL INFERENCE; STATISTICAL RESAMPLING TECHNIQUES;

FORECASTING; INTERNATIONAL TRADE; MATHEMATICAL MODELS; MONTE CARLO METHODS; PARAMETER ESTIMATION; SAMPLING; STATISTICAL TESTS;

FEEDFORWARD NEURAL NETWORKS;

EID: 0035391531     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.935080     Document Type: Article

References (29)

1. Analysis of the clinical variables driving decision in an artificial neural network trained to identify the presence of myocardial infarction
2. Bootstrapping confidence intervals for clinical input variable effects in a network trained to identify the presence of acute myocardial infarction
3. On the geometry of feedforward neural-network error surfaces
4. Improved rates and asymptotic normality for nonparametric neural network estimators
5. Regression by local fitting: Methods, properties, and computational algorithms
6. Topology and geometry of single hidden layer network
7. An introduction to high-frequency finance
8. Nonparametric exchange rate prediction
9. A general approach to Lagrange multiplier model diagnostics
10. The behavior of stock market prices
11. A sharper Bonferroni procedure for multiple tests of significance
12. Asymptotic properties of nonlinear least squares estimators
13. Forecasting exchange rates using feedforward and recurrent neural networks
14. Technical trading rules and regime shifts in foreign exchange
15. High frequency foreign exchange rates: Price behavior analyses and 'true price' models
16. Cross-validation estimates integrated mean squared error
17. Effects of model selection on inference
18. Estimating the dimension of a model
19. Information criteria for selecting possibly misspecified parametric models
20. Uniqueness of the weights for minimal feedforward nets with a given input-output map
21. Learning in artificial neural networks: A statistical perspective