An Exponential-Family Multidimensional Scaling Mixture Methodology

@article{Wedel1996AnEM,
  title={An Exponential-Family Multidimensional Scaling Mixture Methodology},
  author={Michel Wedel and Wayne S. DeSarbo},
  journal={ERN: Statistical Decision Theory; Operations Research (Topic)},
  year={1996}
}
  • M. Wedel, W. DeSarbo
  • Published 1 October 1996
  • Computer Science
  • ERN: Statistical Decision Theory; Operations Research (Topic)
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… 

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References

SHOWING 1-10 OF 36 REFERENCES

A stochastic multidimensional scaling vector threshold model for the spatial representation of “pick any/n” data

This paper presents a new stochastic multidimensional scaling vector threshold model designed to analyze “pick any/n” choice data (e.g., consumers rendering buy/no buy decisions concerning a number

A latent class unfolding model for analyzing single stimulus preference ratings

TLDR
A mixture distribution model is formulated that can be considered as a latent class model for continuous single stimulus preference ratings and is applied to political science data concerning party preferences from members of the Dutch Parliament.

Latent Class Multidimensional Scaling. A Review of Recent Developments in the Marketing and Psychometric Literature

TLDR
Current, state-of-the-art methods for per­forming latent class multidimensional scaling (LCMDS) and the potential application of these methods in marketing, as well as directions for future research, are discussed.

Multiclus: A new method for simultaneously performing multidimensional scaling and cluster analysis

This paper develops a maximum likelihood based method for simultaneously performing multidimensional scaling and cluster analysis on two-way dominance or profile data. This MULTICLUS procedure

A latent class procedure for the structural analysis of two-way compositional data

This paper develops a new procedure for simultaneously performing multidimensional scaling and cluster analysis on two-way compositional data of proportions. The objective of the proposed procedure

Concomitant-Variable Latent-Class Models

Abstract This article introduces and illustrates a new type of latent-class model in which the probability of latent-class membership is functionally related to concomitant variables with known

Analysis of choice behaviour via probabilistic ideal point and vector models

The aim of this paper is to add results to the everlasting attempt to find appropriate models for the description and analysis of choice behaviour. As stochastic generalizations within choice models

Discrete Statistical Models with Social Science Applications.

TLDR
The main subjects of the present book are based on three developments: (1) the theory of exponential families, (2) the Theory of log-linear models, and (3) the theories of logistic models in psychometrics.

Simultaneous multidimensional unfolding and cluster analysis: An investigation of strategic groups

This paper develops a maximum likelihood based methodology for simultaneously performing multidimensional unfolding and cluster analysis on two-way dominance or profile data. This new procedure