A Bayesian Joint Modeling Using Gaussian Linear Latent Variables for Mixed Correlated Outcomes with Possibility of Missing Values
This paper proposes a Bayesian approach for the analysis of mixed correlated nominal, ordinal and continuous outcomes with possibility of missing values using a variation of Markov Chain Monte Carlo (MCMC) method named Parameter Expanded and Reparamerized Metropolis Hastings (PX-RPMH) method. A gene...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2016-11-01
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| Series: | Journal of Statistical Theory and Applications (JSTA) |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25867320.pdf |