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A Bayesian Vector Multidimensional Scaling Procedure Incorporating Dimension Reparameterization with Variable Selection

Published online by Cambridge University Press:  01 January 2025

Duncan K. H. Fong ,
Wayne S. DeSarbo ,
Zhe Chen  and
Zhuying Xu
Duncan K. H. Fong*
Affiliation:
Pennsylvania State University
Wayne S. DeSarbo
Affiliation:
Pennsylvania State University
Zhe Chen
Affiliation:
Pennsylvania State University
Zhuying Xu
Affiliation:
Pennsylvania State University
*
Correspondence should be made to Duncan K. H. Fong, Department of Marketing, Pennsylvania State University, University Park, PA16802, USA. Email: i2v@psu.edu
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Abstract

We propose a two-way Bayesian vector spatial procedure incorporating dimension reparameterization with a variable selection option to determine the dimensionality and simultaneously identify the significant covariates that help interpret the derived dimensions in the joint space map. We discuss how we solve identifiability problems in a Bayesian context that are associated with the two-way vector spatial model, and demonstrate through a simulation study how our proposed model outperforms a popular benchmark model. In addition, an empirical application dealing with consumers’ ratings of large sport utility vehicles is presented to illustrate the proposed methodology. We are able to obtain interpretable and managerially insightful results from our proposed model with variable selection in comparison with the benchmark model.

Keywords

bayesian analysismultidimensional scalingvector modelvariable selectionQR decompositionreparameterizationconsumer psychology

Information

Type
Original Paper
Information
Psychometrika , Volume 80 , Issue 4 , December 2015 , pp. 1043 - 1065
Copyright
Copyright © 2015 The Psychometric Society

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Footnotes

Zhe Chen is currently working at Google Inc.

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