Papers

• Dempster, Laird, and Rubin (1977). "Maximum likelihood from incomplete data via the EM algorithm". JRSSB, 39, 1-38.
• Meng and Rubin (1993) "Maximum likelihood estimation via the ECM algorithm: a general framework". Biometrika, 80, 267-278.
• van Dyk and Meng (2001). The art of data augmentation". Journal of Computational and Graphical Statistics, 10, 1-50.
• Meng and Rubin (1991). "Using EM to obtain asymptotic variance-covariance matrices: the SEM algorithm". JASA, 86, 899-909.
• Meyer (2009) "The bivariate normal Copula".
• Gustafson (2009). "What are the limits of posterior distributions arising form nonidentified models, and why should we care?" JASA, 104, 1682-1695.
• Boldea and Magnus (2009). "Maximum likelihood estimation of the multivariate normal mixture model".JASA, 104, 1539-1549.
• Song, Fan, and Kalbfleisch (2005). "Maximization by parts in likelihood inference". JASA, 100, 1145-1158.
• Chib (1995). "Marginal likelihood from the Gibbs output".JASA, 90, 1313-1321.
• Roberts and Polson (1994). "On the geometric convergence of the Gibbs sampler".JRSSB, 56, 377-384.
• McCulloch (1997). "Maximum likelihood algorithms for generalized linear mixed models".JASA, 92, 162-170.
• Tanner and Wong (1987). "The calculation of posterior distribution by data augmentation". JASA, 82, 528-540.
• Liu and Wu (1999). "Parameter expansion for data augmentation". JASA, 94, 1264-1274.
• Mallick and Gelfand (1994). "Generalized linear models with unknown link functions". Biometrika 81, 237-245.
• Casella and Goerge (1992). "Explaining the Gibbs sampler". The American Statistician 46, 167-174