An Exponential-Family Multidimensional Scaling Mixture Methodology
Journal of Business & Economic Statistics, Vol. 14, No. 4, pp. 447-459
Posted: 8 Jun 2016
Date Written: October 1996
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.
Keywords: Concomitant variable model, EM algorithm, Maximum likelihood, Unfolding
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