Heterogeneity in consumer preference data -a combined approach

Food Quality and Preference

Volumn 8, Issue 5-6, 1997, Pages 409-417

  Poulsen, Carsten Stig ^a   Bruun Brockhoff, Per M ^b   Erichsen, Lars ^b  

^a AALBORG UNIVERSITY   (Denmark)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

PISUM SATIVUM;

EID: 0031457415     PISSN: 09503293     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0950-3293(97)00060-8     Document Type: Article

References (23)

1. Arminger, G., Stein, P. and Wittenberg, J. (1995) Mixtures of conditional mean-and covariance structure models. Unpublished working paper.
2. Bech, A. C., Hansen, M. and Wienberg, L. (1997) Application of house of quality in translation of consumer demands into sensory attributes measurable by descriptive sensory analysis. Food Quality and Preference. 8, 329-348
3. Bozdogan, H. (1987) Model selection and Akaike's Information Criterion (AIC): the general theory and its analytical extensions. Psychometrika 52(3), 345-370.
4. Brockhoff, P. M. (1996) Growth curve modelling. Sensometrics Workshop, 2nd Rose Marie Pangborn Sensory Symposium, Question 4. To appear in Food Quality and Preference.
5. Brockhoff, P. M. and Erichsen, L. (1996) Latent class random coefficient regression. Working paper, Department of Mathematics and Physics, Royal Veterinary and Agricultural University, Denmark.
6. Callier, P. and Schlich, P. (1995) Extension de la cartographie des preferences aux plans incomplets. In Proceedings of the 4th European Conference on Food Industry and Statistics, Dijon, December 7-8 , 149-159.
7. Day, N. E. (1969) Estimating the components of a mixture of normal distributions. Biometrika 56, 463-474.
8. Dempster, A. P., Laird, N. M. and Rubin, R. B. (1977) Maximum likelihood from incomplete data via the EM-algorithm. Journal of the Royal Statistical Society Ser. B 39, 1-38.
9. Jedidi, K., Jagpal, H. S. and DeSarbo, W. S. (1997) Finite-mixture equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16, 39-59.
10. Johansen, S. (1983) Some topics in regression. Scandinavian Journal of Statistics 10, 161-194.
11. Jöreskog, K. G. and Sörbom, D. (1993) LISREL 8: Structural Equation Modelling with the SIMPLIS Command Language. Scientific Software International, Chicago, USA.
12. Longford, N. T. (1993) Random Coeffcient Models. Clarendon Press, Oxford.
13. Martens, H. and Næs, T. (1989) Multivariate Calibration. Willy, Chichester.
14. McEwan, J. A. (1996) Preference mapping for product optimization. In Multivariate Analysis of Data in Sensory Science, eds T. Næs and E. Risvik. Elsevier Science, Amsterdam.
15. McLachlan, G. J. (1987) On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture. Applied Statistics 36(3), 318-324.
16. McLachlan, G. J. and Basford, K. E. (1988) Mixture Models: Inference and Applications to Clustering. Marcel Dekker, New York.
17. Muthén, B. O. (1989) Latent variable modeling in heterogeneous Populations. Psychometrika 54(4), 557-585.
18. Muthén, B. O. and Satorra, S. (1989) Multilevel aspects of varying parameters in structural models. In Multilevel Analysis of Educational Data, ed. R. D. Bock.
19. Næs, T. and Martens, H. (1985) Comparison of prediction methods for multicollinear data. Commun. Statict. - Simula. -Comp. Uta., 14, 545-576.
20. Titterington, D. M., Smith, A. F. M. and Makov, U. E. (1985) Statistical Analysis of Finite Mixture Distributions. John Wiley, New York.
21. von Rosén, D. (1991) The growth curve model: a review. Communications in Statistical Theory and Methods 20(9), 2791-2822.
22. Wedel, M. and DeSarbo, W. S. (1994) A review of recent developments in latent class regression models. In Advanced Methods for Marketing Research, ed. R. P. Bogozzi. pp. 353-388.
23. Wedel, M. and DeSarbo, W. S. (1995) A mixture likelihood