References
- Akaike, H. 1974. A new look at the statistical model identification. IEEE transactions on automatic control 19:716–23.
- Brooks, S. P., P. Giudici, and A. Philippe. Nonparametric convergence assessment for MCMC model selection. Journal of Computational and Graphical Statistics 12:1–22.
- Casella, G., and R. L. Berger. 2002. Statistical inference. Pacific Grove, CA: Duxbury.
- Christensen, R., J. Wesley, B. Adam, and H. Timothy. 2011. Bayesian ideas and data analysis: An introduction for scientists and statisticians. Boca Raton: CRC Press.
- Dellaportas, P., J. J. Forster, and I. Ntzoufras. 2002. On Bayesian model and variable selection using MCMC. Statistics and Computing 12:27–36.
- Diggle, P., and M. G. Kenward. 1994. Informative drop-out in longitudinal data analysis. Applied Statistics 1:49–93.
- Ganjali, M., and T. Baghfalaki. 2014. A Bayesian shared parameter model for analysing longitudinal skewed responses with nonignorable dropout. International Journal of Statistics in Medical Research 3 (2):103–115.
- Green, P. J. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82:711–32.
- Hastings, W. K. 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57 (1):97–109.
- Heckman, J. J. 1976. The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement 5:475–92.
- Kass, R. E., and A. E. Raftery. 1995. Bayes factors. Journal of the American Statistical Association 90:773–95.
- Konishi, S., and G. Kitagawa. 2008. Information criteria and statistical modeling. New York: Springer Science & Business Media.
- McKinley, T. J., M. Morters, and J. L. Wood. 2015. Bayesian Model Choice in Cumulative Link Ordinal Regression Models. Bayesian Analysis 10:1–30.
- Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, anb E. Teller. 1953. Equation of state calculations by fast computing machines. The Journal of Chemical Physics 21:1087–92.
- Molenberghs, G., and M. Kenward. 2007. Missing data in clinical studies. Chichester, UK: John Wiley & Sons, 2007.
- Richardson, S., and P. J. Green. 1997. On Bayesian analysis of mixtures with an unknown number of components (with discussion). Journal of the Royal Statistical Society: Series B (Statistical Methodology) 59:731–792.
- Rubin, D. B. 1976. Inference and missing data. Biometrika 63:581–92.
- Scaccia, L., and P.J. Green. 2003. Bayesian growth curves using normal mixtures with nonparametric weights. Journal of Computational and Graphical Statistics 12:308–31.
- Schwarz, G. 1978. Estimating the dimension of a model. The Annals of Statistics 6:461–64.
- Spiegelhalter, D. J., G. B. Nicola, P. C. Bradley, and L. Angelika. 2014. The deviance information criterion. Journal of the Royal Statistical Society(Statistical Methodology) 76:485–93.