An Exponential-Family Multidimensional Scaling Mixture Methodology

Bibliographic Data

ID19418685
AuthorsMichel Wedel (0000-0002-1244-4923, University of Groningen), Wayne S Desarbo (Pennsylvania State University)
Year1996
Volume14
Issue4
Pages447-459
Publication date1996-10-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.1996.10524674
OpenAlexW2003597529
LanguageEN
Citations received3
References cited28

A multidimensional scaling methodology (STUNMIX) for the analysis of subjects' preference/choice of stimuli that sets out to integrate the previous work in this area into a single framework, as well as to provide a variety of new options and models, is presented. 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, whereby a wide range of different data types can be analyzed. Possible reparameterizations 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. The performance of the models is demonstrated on synthetic data, and robustness is investigated. An empirical application is provided, concerning intentions to buy portable telephones. KEY WORDS: Concomitant variable modelEM algorithmMaximum likelihoodUnfolding

Cognitive psychology · Econometrics · Exponential family · Exponential function · Latent variable · Multidimensional scaling · Scaling · Statistics · Bayesian Methods and Mixture Models · Computer Science · Consumer Market Behavior and Pricing · Mathematics · Psychology · Sensory Analysis and Statistical Methods

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