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International Conference on Artificial Neural Networks

ICANN 2001: Artificial Neural Networks — ICANN 2001 pp 790–795Cite as

Analyzing Purchase Data by a Neural Net Extension of the Multinomial Logit Model

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2130))

Abstract

Brand choice models used to analyze households purchase data as a rule have a linear (deterministic) utility function, i.e. they conceive utility as linear combination of predictors like price, sales promotion variables, brand name and other product attributes. To discover nonlinear effects on brands’ utilities in a flexible way we specify deterministic utility by means of a certain type of neural net. This feedforward multilayer perceptron is able to approximate any continuous multivari-ate function and its derivatives with the desired level of precision. In an empirical study the neural net based choice model leads to better results with regard to both estimation and test data, and implies different choice elasticities.

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Author information

Authors and Affiliations

  1. University of Regensburg, D-93053, Regensburg, Germany

    Harald Hruschka & Markus Probst

  2. debis Systemhaus GEI, D-80992, Munich, Germany

    Werner Fettes

Authors
  1. Harald Hruschka
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  2. Werner Fettes
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  3. Markus Probst
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Editor information

Editors and Affiliations

  1. Dept. of Mecidal Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6/2, 1010, Vienna, Austria

    Georg Dorffner

  2. Institute for Computer Aided Automation Pattern Recognition and Image Processing Group, Technical University of Vienna, Favoritenstr. 9/1832, 1040, Vienna, Austria

    Horst Bischof

  3. Institut für Statistik, Wirtschaftsuniversität Wien, Augasse 2-6, 1090, Wien, Austria

    Kurt Hornik

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© 2001 Springer-Verlag Berlin Heidelberg

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Hruschka, H., Fettes, W., Probst, M. (2001). Analyzing Purchase Data by a Neural Net Extension of the Multinomial Logit Model. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_110

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  • DOI: https://doi.org/10.1007/3-540-44668-0_110

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42486-4

  • Online ISBN: 978-3-540-44668-2

  • eBook Packages: Springer Book Archive

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