Explaining consumer choice through neural networks: The stacked generalization approach
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Dasgupta, Chanda Ghose & Dispensa, Gary S. & Ghose, Sanjoy, 1994. "Comparing the predictive performance of a neural network model with some traditional market response models," International Journal of Forecasting, Elsevier, vol. 10(2), pages 235-244, September.
- Min, Chung-ki & Zellner, Arnold, 1993.
"Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates,"
Journal of Econometrics,
Elsevier, vol. 56(1-2), pages 89-118, March.
- Min, C.K. & Zellner, A., 1992. ""Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rates"," Papers 90-92-23, California Irvine - School of Social Sciences.
- Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
- Hui, Michael K & Bateson, John E G, 1991. " Perceived Control and the Effects of Crowding and Consumer Choice on the Service Experience," Journal of Consumer Research, Oxford University Press, vol. 18(2), pages 174-184, September.
- Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
- Patricia M. West & Patrick L. Brockett & Linda L. Golden, 1997. "A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice," Marketing Science, INFORMS, vol. 16(4), pages 370-391.
- Simonson, Itamar & Winer, Russell S, 1992. " The Influence of Purchase Quantity and Display Format on Consumer Preference for Variety," Journal of Consumer Research, Oxford University Press, vol. 19(1), pages 133-138, June.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- van Wezel, Michiel & Potharst, Rob, 2007. "Improved customer choice predictions using ensemble methods," European Journal of Operational Research, Elsevier, vol. 181(1), pages 436-452, August.
- repec:eco:journ1:2017-03-17 is not listed on IDEAS
- van Wezel, M.C. & Potharst, R., 2005. "Improved customer choice predictions using ensemble methods," Econometric Institute Research Papers EI 2005-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Potharst, R. & van Rijthoven, M. & van Wezel, M.C., 2005. "Modeling brand choice using boosted and stacked neural networks," Econometric Institute Research Papers EI 2005-05, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
More about this item
StatisticsAccess and download statistics
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:146:y:2003:i:3:p:650-660. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/eor .
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.