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An Exponential-Family Multidimensional Scaling Mixture Methodology

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  • Wedel, Michel
  • DeSarbo, Wayne S

Abstract

A multidimensional scaling methodology (STUNMIX) for the analysis of subjects preference/choice of stimuli is presented, which integrates previous models into a single framework. Locations of the stimuli and the ideal-points of derived segments of subjects on latent dimensions are estimated simultaneously. The methodology is formulated in the framework of the exponential family of distributions. Possible reparametrizations of stimulus coordinates by stimulus characteristics, as well as of probabilities of segment membership by subject background variables, are permitted. The models are estimated in a maximum likelihood framework.

Suggested Citation

  • Wedel, Michel & DeSarbo, Wayne S, 1996. "An Exponential-Family Multidimensional Scaling Mixture Methodology," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 447-459, October.
    • Handle: RePEc:bes:jnlbes:v:14:y:1996:i:4:p:447-59
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    Citations

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    Cited by:

    1. DeSarbo, Wayne S. & Selin Atalay, A. & Blanchard, Simon J., 2009. "A three-way clusterwise multidimensional unfolding procedure for the spatial representation of context dependent preferences," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3217-3230, June.
    2. Junghyun Park & Minki Kim & Pradeep K Chintagunta, 2022. "Mapping Consumers’ Context-Dependent Consumption Preferences: A Multidimensional Unfolding Approach [An Empirical Comparison of Logit Choice Models with Discrete versus Continuous Representations o," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 49(2), pages 202-228.
    3. Joonwook Park & Wayne DeSarbo & John Liechty, 2008. "A Hierarchical Bayesian Multidimensional Scaling Methodology for Accommodating Both Structural and Preference Heterogeneity," Psychometrika, Springer;The Psychometric Society, vol. 73(3), pages 451-472, September.
    4. DeSarbo, Wayne S. & Kim, Youngchan & Wedel, Michel & Fong, Duncan K. H., 1998. "A Bayesian approach to the spatial representation of market structure from consumer choice data," European Journal of Operational Research, Elsevier, vol. 111(2), pages 285-305, December.
    5. Wayne S. DeSarbo & Alexandru M. Degeratu & Michel Wedel & M. Kim Saxton, 2001. "The Spatial Representation of Market Information," Marketing Science, INFORMS, vol. 20(4), pages 426-441, June.
    6. Tammo H.A. Bijmolt & Michel Wedel & Wayne S. DeSarbo, 2021. "Adaptive Multidimensional Scaling: Brand Positioning Based on Decision Sets and Dissimilarity Judgments," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(1), pages 1-15, June.
    7. Joonwook Park & Priyali Rajagopal & Wayne DeSarbo, 2012. "A New Heterogeneous Multidimensional Unfolding Procedure," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 263-287, April.
    8. Wayne DeSarbo & Robert Madrigal, 2012. "Exploring the Demand Aspects of Sports Consumption and Fan Avidity," Interfaces, INFORMS, vol. 42(2), pages 199-212, April.
    9. Martin Natter & Andreas Mild & Udo Wagner & Alfred Taudes, 2008. "—Planning New Tariffs at tele.ring: The Application and Impact of an Integrated Segmentation, Targeting, and Positioning Tool," Marketing Science, INFORMS, vol. 27(4), pages 600-609, 07-08.
    10. Michel Wedel, 2001. "Computing the Standards Errors of Mixture Model Parameters with EM when Classes are Well Separated," Computational Statistics, Springer, vol. 16(4), pages 539-558, December.
    11. J. Vera & Rodrigo Macías & Willem Heiser, 2013. "Cluster Differences Unfolding for Two-Way Two-Mode Preference Rating Data," Journal of Classification, Springer;The Classification Society, vol. 30(3), pages 370-396, October.
    12. Rick L. Andrews & Ajay K. Manrai, 1999. "MDS Maps for Product Attributes and Market Response: An Application to Scanner Panel Data," Marketing Science, INFORMS, vol. 18(4), pages 584-604.
    13. J. Vera & Rodrigo Macías & Willem Heiser, 2009. "A Latent Class Multidimensional Scaling Model for Two-Way One-Mode Continuous Rating Dissimilarity Data," Psychometrika, Springer;The Psychometric Society, vol. 74(2), pages 297-315, June.
    14. Douglas Clarkson & Richard Gonzalez, 2001. "Random effects diagonal metric multidimensional scaling models," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 25-43, March.

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