Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling

Kensuke Okada () and Shin-ichi Mayekawa
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Kensuke Okada: Senshu University
Shin-ichi Mayekawa: Tokyo Institute of Technology

Computational Statistics, 2018, vol. 33, issue 3, No 17, 1457-1473

Abstract: Abstract In Bayesian analysis of multidimensional scaling model with MCMC algorithm, we encounter the indeterminacy of rotation, reflection and translation of the parameter matrix of interest. This type of indeterminacy may be seen in other multivariate latent variable models as well. In this paper, we propose to address this indeterminacy problem with a novel, offline post-processing method that is easily implemented using easy-to-use Markov chain Monte Carlo (MCMC) software. Specifically, we propose a post-processing method based on the generalized extended Procrustes analysis to address this problem. The proposed method is compared with four existing methods to deal with indeterminacy thorough analyses of artificial as well as real datasets. The proposed method achieved at least as good a performance as the best existing method. The benefit of the offline processing approach in the era of easy-to-use MCMC software is discussed.

Keywords: MCMC estimation; Offline post-processing; Latent variable modeling; MDS (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s00180-017-0759-6

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