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

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  • Duncan Fong
  • Wayne DeSarbo
  • Zhe Chen
  • Zhuying Xu

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. Copyright The Psychometric Society 2015

Suggested Citation

  • Duncan Fong & Wayne DeSarbo & Zhe Chen & Zhuying Xu, 2015. "A Bayesian Vector Multidimensional Scaling Procedure Incorporating Dimension Reparameterization with Variable Selection," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 1043-1065, December.
    • Handle: RePEc:spr:psycho:v:80:y:2015:i:4:p:1043-1065
    DOI: 10.1007/s11336-015-9449-x
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    Cited by:

    1. Lin, L. & Fong, D.K.H., 2019. "Bayesian multidimensional scaling procedure with variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 1-13.

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