Skip to main content
SpringerLink
Log in

Neural Network Modeling by Subsampling

  • Conference paper
Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

Included in the following conference series:

Abstract

The aim of the paper is to develop hypothesis testing procedures both for variable selection and model adequacy to facilitate a model selection strategy for neural networks. The approach, based on statical inference tools, uses the subsampling to overcome the analytical and probabilistic difficulties related to the estimation of the sampling distribution of the test statistics involved. Some illustrative examples are also discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anders, U., Korn, O.: Model selection in neural networks. Neural Networks 12, 309–323 (1999)

    Article  Google Scholar 

  2. Barron, A.R.: Universal approximation bounds for superposition of a sigmoidal function. IEEE Transactions on Information Theory 39, 930–945 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  3. Cottrell, M., Girard, B., Girard, Y., Mangeas, M., Muller, C.: Neural modeling for time series: a statistical stepwise method for weight elimination. IEEE Transaction on Neural Networks 6, 1355–1364 (1995)

    Article  Google Scholar 

  4. Fukumizu, K.: Likelihood Ratio of Unidentifiable Models and Multilayer Neural Networks. The Annals of Statistics 31, 833–851 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  5. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg

    Google Scholar 

  6. La Rocca, M., Perna, C.: Variable selection in neural network regression models with dependent data: a subsampling approach. Computational Statistics and Data Analysis 48, 415–419 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  7. Lee, T.H., White, H., Granger, C.W.J.: Testing for neglected nonlinearity in time series models. Journal of Econometrics 56, 269–290 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  8. Politis, D.N., Romano, J.P., Wolf, M.: Subsampling for heteroscedastic time series. Journal of Econometrics 81, 281–317 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  9. Politis, D.N., Romano, J.P., Wolf, M.: Subsampling. Springer, NY (1999)

    MATH  Google Scholar 

  10. Reed, R.: Pruning algorithms-a survey. Neural Networks 4, 740–747 (1993)

    Article  Google Scholar 

  11. Refenes, A.P.N., Zapranis, A.D.: Neural model identification, variable selection and model adeguacy. Journal of Forecasting 18, 299–332 (1999)

    Article  Google Scholar 

  12. Teraesvirta, T., Lin, C.F., Granger, C.W.J.: Power of the Neural Network Linearity Test. Journal of Time Series Analysis 14, 209–220 (1993)

    Article  Google Scholar 

  13. White, H., Racine, J.: Statistical Inference, The Bootstrap, and Neural-Network Modeling with Application to Foreign Exchange Rates. IEEE Transactions on Neural Networks 12, 657–673 (2001)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Economics and Statistics, University of Salerno, Via Ponte don Melillo, 84084, Fisciano, SA, Italy

    Michele La Rocca & Cira Perna

Authors
  1. Michele La Rocca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cira Perna
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Department of Computer Architecture and Computer Technology, University of Granada,  

    Alberto Prieto

  3. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

La Rocca, M., Perna, C. (2005). Neural Network Modeling by Subsampling. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_25

Download citation

  • DOI: https://doi.org/10.1007/11494669_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation