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}
}
  • R. Andrews, Asim M. Ansari, Imran S. Currim
  • Published 2002
  • Mathematics
  • Journal of Marketing Research
  • 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
    175 Citations

    Tables from this paper

    I A Hierarchical Bayes Model for Ranked Conjoint Data
    • Highly Influenced
    • PDF
    A new model with rating-based conjoint analysis
    Predicting Purchase Decisions with Different Conjoint Analysis Methods: A Monte Carlo Simulation
    • 27
    • Highly Influenced
    A Comparison of Two-Stage Segmentation Methods for Choice-Based Conjoint Data: A Simulation Study
    • 2
    • Highly Influenced
    • PDF

    References