Bayesian multidimensional scaling procedure with variable selection

L. Lin and D.K.H. Fong

Computational Statistics & Data Analysis, 2019, vol. 129, issue C, 1-13

Abstract: Multidimensional scaling methods are frequently used by researchers and practitioners to project high dimensional data into a low dimensional space. However, it is a challenge to integrate side information which is available along with the dissimilarities to perform such dimension reduction analysis. A novel Bayesian integrative multidimensional scaling procedure, namely Bayesian multidimensional scaling with variable selection, is proposed to incorporate external information on the objects into the analysis through the use of a latent multivariate regression structure. The proposed Bayesian procedure allows the incorporation of covariate information into the dimension reduction analysis through the use of a variable selection strategy. An efficient computational algorithm to implement the procedure is also developed. A series of simulation experiments and a real data analysis are conducted, and the proposed model is shown to outperform several benchmark models based on some measures commonly used in the literature.

Keywords: Bayesian multidimensional scaling; Variable selection; Model selection; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:129:y:2019:i:c:p:1-13

DOI: 10.1016/j.csda.2018.07.007

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