Hierarchical Bayes versus Finite Mixture Conjoint Analysis Models: A Comparison of Fit, Prediction, and Partworth Recovery
@article{Andrews2002HierarchicalBV, title={Hierarchical Bayes versus Finite Mixture Conjoint Analysis Models: A Comparison of Fit, Prediction, and Partworth Recovery}, author={R. Andrews and Asim M. Ansari and Imran S. Currim}, journal={Journal of Marketing Research}, year={2002}, volume={39}, pages={87 - 98} }
A study conducted by Vriens, Wedel, and Wilms (1996) and published in Journal of Marketing Research found that finite mixture (FM) conjoint models had the best overall performance of nine conjoint segmentation methods in terms of fit, prediction, and parameter recovery. Since that study, hierarchical Bayes (HB) conjoint analysis methods have been proposed to estimate individual-level partworths and have received much attention in the marketing research literature. However, no study has compared… CONTINUE READING
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References
SHOWING 1-10 OF 25 REFERENCES
Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs
- Mathematics
- 1996
- 465
Using Segmentation Approaches for Better Prediction and Understanding from Consumer Mode Choice Models
- Computer Science
- 1981
- 100
Mixture-Model Cluster Analysis Using Model Selection Criteria and a New Informational Measure of Complexity
- Computer Science
- 1994
- 187
Computational and Inferential Difficulties with Mixture Posterior Distributions
- Mathematics
- 2000
- 616
- PDF