Skip to main content

A Statistical Methodology for Specifying Neural Network Models: Application to the Identification of Cross-Selling Opportunities

  • Chapter
Bio-Mimetic Approaches in Management Science

Part of the book series: Advances in Computational Management Science ((AICM,volume 1))

  • 56 Accesses

Abstract

Examining the recent applications of neurotechnology in the marketing field, one realises that the focus tends to be on comparing the predictive performance of neural networks to that of statistical models. The question addressed in most studies is “are neural networks better than statistical techniques ?”, and the published results appear to be both encouraging and discouraging (e.g. Furness 1995, Ripley 1994).

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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

  • Aldrich J, Nelson F. Linear probability, logit, and probit models. Sage Publications, 1984.

    Google Scholar 

  • Bent, Y, Merunka D. Modelling brand choice with the multinomial logit model and neural networks: a comparison of methods and results. Proceedings of the Second International Meeting on Neural Approaches in Economics and Management Science ( ANSEG) Poitiers, France, 1995.

    Google Scholar 

  • Burgess AN. Methodologies for neural network systems. Proceedings of the Henry Stewart Conference on Neural Networks in Marketing, London, U.K. 1995.

    Google Scholar 

  • Fahlman S, Lebiere C. “The cascade-correlation learning architecture.” In Advances in Neural Information Processing Systems 2, D.S.Touretzky ed., Morgan Kaufmann, Los Altos CA: 1990; 524–532.

    Google Scholar 

  • Furness P. “Neural networks for data-driven marketing.” In Intelligent Systems for Finance and Business, S.Goonatilake and P.Treleaven eds., 1995; 73–96.

    Google Scholar 

  • Hauser JR. Testing the Accuracy, Usefulness, and Significance of Probabilistic Choice Models: an Information Theoretic Approach. Operations Research, 1978; 26: 406–421.

    Article  Google Scholar 

  • Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators.Neural Networks, 1989; 2: 359–366.

    Article  Google Scholar 

  • Ripley BD. Neural networks and related methods for classification. Journal of the Royal Statistical Society, B, 1994; 56, 3: 409–456.

    Google Scholar 

  • Rumelhart DE, McClelland JL. Parallel Distributed Processing. MIT Press, 1986.

    Google Scholar 

  • Verley G, Asselin de Beauville JP. Multilayer Perceptron Learning Control. Proceedings of EURO-PAR `96, Ecole Normale Superieure, Lyon, August 1996; 27–29.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Burgess, A.N., Pandelidaki, S. (1998). A Statistical Methodology for Specifying Neural Network Models: Application to the Identification of Cross-Selling Opportunities. In: Aurifeille, JM., Deissenberg, C. (eds) Bio-Mimetic Approaches in Management Science. Advances in Computational Management Science, vol 1. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2821-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-2821-7_9

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4791-8

  • Online ISBN: 978-1-4757-2821-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics