Bayesian Estimation and Model Selection of Multivariate Linear Model with Polytomous Variables

Multivariate Behavioral Research
Xin-Yuan Song, Sik-Yum Lee

Abstract

This article provides a Bayesian analysis of the multivariate linear model with polytomous variables. The computational burden due to the intractable multiple integrals induced by the polytomous variables and the model is solved by augmenting the underlying latent continuous measurements of the observed polytomous data. A Gibbs sampler algorithm is implemented to produce the Bayesian estimate. A Bayes factor approach is proposed for model selection, and it is approximated by the Bayesian information criterion (BIC) via the bridge sampling. The proposed methodology is illustrated by examples using multivariate linear regression and multivariate two-way analysis of variance with real data.

References

Nov 1, 1995·The British Journal of Mathematical and Statistical Psychology·S Y LeeP M Bentler
Dec 8, 2000·The British Journal of Mathematical and Statistical Psychology·S Y Lee, H T Zhu
Jan 31, 2002·The British Journal of Mathematical and Statistical Psychology·X Y Song, S Y Lee
Jun 1, 1984·IEEE Transactions on Pattern Analysis and Machine Intelligence·S Geman, D Geman
Oct 1, 2001·Multivariate Behavioral Research·S Y Lee, X Y Song

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Citations

Jul 10, 2010·International Journal of Environmental Research and Public Health·Miguel A NegrínFrancisco J Girón
Sep 16, 2003·Statistics in Medicine·Sik-Yum Lee, Xin-Yuan Song
Jan 1, 2003·Multivariate Behavioral Research·Xin-Yuan Song, Sik-Yum Lee

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