A Bayesian Joint Modeling Using Gaussian Linear Latent Variables for Mixed Correlated Outcomes with Possibility of Missing Values

DOI

10.2991/jsta.2016.15.4.5How to use a DOI?

Keywords

Mixed Data; Correlated Outcomes; Parameter Expansion; MCMC; Data Augmentation; Longitudinal Data.

Abstract

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 general latent variable model is given and a Gibbs sampler is developed to incorporate PX-RPMH and Data Augmentation (DA) steps, to estimate parameters and to impute missing values. The performance of the algorithm is evaluated by some simulation studies. An application of the model to the foreign language attitude scale dataset is also enclosed.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Sayed Jamal Mirkamali
AU  - Mojtaba Ganjali
PY  - 2016
DA  - 2016/12/01
TI  - A Bayesian Joint Modeling Using Gaussian Linear Latent Variables for Mixed Correlated Outcomes with Possibility of Missing Values
JO  - Journal of Statistical Theory and Applications
SP  - 373
EP  - 386
VL  - 15
IS  - 4
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2016.15.4.5
DO  - 10.2991/jsta.2016.15.4.5
ID  - Mirkamali2016
ER  -