Modeling brand choice using boosted and stacked neural networks

Business Applications and Computational Intelligence

Volumn , Issue , 2005, Pages 71-90

  Potharst, Rob ^a   van Rijthoven, Michiel ^b   van Wezel, Michiel C ^a  

^a ERASMUS UNIVERSITY ROTTERDAM   (Netherlands)

^b Oracle Nederland BV   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84898378395     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59140-702-7.ch005     Document Type: Chapter

References (19)

1. Chintagunta, P. K., & Prasad, A. R. (1998). An empirical investigation of the dynamic McFadden model of purchase timing and brand choice: Implications for market structure. Journal of Business and Economic Statistics, 16, 2-11.
2. Dasgupta, C. G., Dispensa, G. S., & Ghose, S. (1994). Comparing the predictive performance of a neural network model with some traditional market response models. International Journal of Forecasting, 10, 235-244.
3. Franses, P. H., & Paap, R. (2001). Quantitative models in marketing research. Cambridge, UK: Cambridge University.
4. Freund, Y., & Schapire, R. (1996). Experiments with a new boosting algorithm. In L. Saitta (Ed.), Proceedings of the Thirteenth International Conference on Machine Learning (pp. 148-156). San Francisco: Morgan Kaufmann.
5. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning. New York: Springer-Verlag.
6. Hruschka, H. (1993). Determining market response functions by neural network modeling: A comparison to econometric techniques. European Journal of Operational Research, 66, 27-35.
7. Hruschka, H., Fettes, W., Probst, M., & Mies, C. (2002). A flexible brand choice model based on neural net methodology. A comparison to the linear utility multinomial logit model and its latent class extension. OR Spectrum, 24, 127-143.
8. Hu, M. Y., Shanker, M., & Hung, M. S. (1999). Estimation of posterior probabilities of consumer situational choices with neural network classifiers. International Journal of Research in Marketing, 16, 307-317.
9. Hu, M. Y., & Tsoukalas, C. (2003). Explaining consumer choice through neural networks: The stacked generalization approach. European Journal of Operational Research, 146, 650-660.
10. McFadden, D. (1973). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 105-142). New York: Academic.
11. Potharst, R., Kaymak, U., & Pijls, W. (2002). Neural networks for target selection in direct marketing. In K. Smith & J. Gupta (Eds.), Neural networks in business: Techniques and applications (pp. 89-110). Hershey, PA: Idea Group Publishing.
12. Rijthoven, M. P. S. V (2004). The use of neural network boosting in a marketing environment. Masters Thesis, Erasmus University.
13. Roberts, J. H., & Lattin, J. M. (1997). Consideration: Review of research and prospects for future insights. Journal of Marketing Research, 34, 406-410.
14. Schwenk, H., & Bengio, Y. (2000). Boosting neural networks. Neural Computation, 12, 1869-1887.
15. Smith, K., & Gupta, J. (Eds.). (2002). Neural networks in business: Techniques and applications. Hershey, PA: Idea Group Publishing.
16. Tibshirani, R., Friedman, J., & Hastie, T. (2000). Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28, 337-407.
17. Vroomen, B., Franses, P. H., & van Nierop, E., (2004). Modeling consideration sets and brand choice using artificial neural networks. European Journal of Operational Research, 154, 206-217.
18. West, P. M., Brockett, P. L., & Golden, L. L. (1997). A comparative analysis of neural networks and statistical methods for predicting consumer choice. Marketing Science, 16(4), 370-391.
19. Wezel, M. V, & Potharst, R. (2005). Improved customer choice predictions using ensemble methods. Technical Report, Erasmus University, Econometric Institute EI 2005-08.