A Bayesian Vector Multidimensional Scaling Procedure Incorporating Dimension Reparameterization with Variable Selection.
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.
Department of Marketing, Pennsylvania State University, University Park, PA, 16802, USA. i2v@psu.edu.
Duncan K H
DK
Fong
Department of Marketing, Pennsylvania State University, University Park, PA, 16802, USA. wsd6@psu.edu.
Wayne S
WS
DeSarbo
Department of Statistics, Pennsylvania State University, University Park, PA, 16802, USA. zhec@google.com.
Zhe
Z
Chen
Department of Statistics, Pennsylvania State University, University Park, PA, 16802, USA. xu.zhuying@gmail.com.
Zhuying
Z
Xu
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D015233:Models, Statistical; D011594:Psychometrics / Q000706:statistics & numerical data
QR decomposition; bayesian analysis; consumer psychology; multidimensional scaling; reparameterization; variable selection; vector model