An Exponential-Family Multidimensional Scaling Mixture Methodology

Journal of Business & Economic Statistics, Vol. 14, No. 4, pp. 447-459

Posted: 8 Jun 2016

See all articles by Michel Wedel

Michel Wedel

University of Maryland - Robert H. Smith School of Business, Marketing Department; University of Groningen - Faculty of Economics and Business

Wayne S. DeSarbo

Pennsylvania State University

Date Written: October 1996

Abstract

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

Suggested Citation

Wedel, Michel and DeSarbo, Wayne S., An Exponential-Family Multidimensional Scaling Mixture Methodology (October 1996). Journal of Business & Economic Statistics, Vol. 14, No. 4, pp. 447-459. Available at SSRN: https://ssrn.com/abstract=2791191

Michel Wedel

University of Maryland - Robert H. Smith School of Business, Marketing Department ( email )

College Park, MD 20742
United States
301.405.2162 (Phone)
301.405.0146 (Fax)

HOME PAGE: http://www.rhsmith.umd.edu/marketing/faculty/wedel.html

University of Groningen - Faculty of Economics and Business ( email )

Postbus 72
9700 AB Groningen
Netherlands

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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