Albert, J. H., & Chib, S.  (2001). Sequential ordinal modeling with applications to survival data. Biometrics, 57(3), 829-836.

   Burgette, L. F., & Nordheim, E. V.  (2012). The trace restriction: An alternative identification strategy for the Bayesian multinomial probit model. Journal of Business & Economic Statistics, 30(3), 404-410.

   Chib, S.  (1995). Marginal likelihood from the Gibbs output. Journal of the American Statistical Association, 90(432), 1313-1321.

   Chib, S.  (2001). Markov chain Monte Carlo methods: Computation and inference. In J. J. Heckman & E. E. Leamer (Eds.), Handbook of econometrics (Vol. 5, p. 3569-3649). Elsevier.

   Chib, S., & Jeliazkov, I.  (2001). Marginal likelihood from the Metropolis-Hastings output. Journal of the American Statistical Association, 96(453), 270-281.

   Emvalomatis, G.  (2011). Adjustment and unobserved heterogeneity in dynamic stochastic frontier models. Journal of Productivity Analysis, 37(1), 7-16.

   Greenberg, E.  (2013). Introduction to bayesian econometrics (2nd ed.). New York, NY: Cambridge University Press.

   Greene, W.  (2004). Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization’s panel data on national health care systems. Health Economics, 13(10), 959-980.

   Koop, G.  (2003). Bayesian econometrics. Chichester, UK: John Wiley & Sons.

   Lancaster, T.  (2004). An introduction to modern bayesian econometrics. Oxford, UK: Wiley-Blackwell.

   Lewis, S. M., & Raftery, A. E.  (1997). Estimating Bayes factors via posterior simulation with the Laplace-Metropolis estimator. Journal of the American Statistical Association, 92(438), 648-655.

   Malik, H. J., & Abraham, B.  (1973). Multivariate logistic distributions. The Annals of Statistics, 1(3), 588-590.

   Tsionas, E. G.  (2006). Inference in dynamic stochastic frontier models. Journal of Applied Econometrics, 21(5), 669–676.