Ascent‐based Monte Carlo expectation– maximization
暂无分享,去创建一个
[1] Maurice G. Kendall. The advanced theory of statistics , 1958 .
[2] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[3] T. Louis. Finding the Observed Information Matrix When Using the EM Algorithm , 1982 .
[4] New York Dover,et al. ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHM , 1983 .
[5] E. Nummelin. General irreducible Markov chains and non-negative operators: Embedded renewal processes , 1984 .
[6] G. C. Wei,et al. A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms , 1990 .
[7] Boris Polyak,et al. Acceleration of stochastic approximation by averaging , 1992 .
[8] Richard L. Tweedie,et al. Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.
[9] C. Geyer. On the Convergence of Monte Carlo Maximum Likelihood Calculations , 1994 .
[10] C. McCulloch. Maximum Likelihood Variance Components Estimation for Binary Data , 1994 .
[11] Bin Yu,et al. Regeneration in Markov chain samplers , 1995 .
[12] R. Littell. SAS System for Mixed Models , 1996 .
[13] Ross Ihaka,et al. Gentleman R: R: A language for data analysis and graphics , 1996 .
[14] C. McCulloch. Maximum Likelihood Algorithms for Generalized Linear Mixed Models , 1997 .
[15] C. Geyer,et al. Geometric Ergodicity of Gibbs and Block Gibbs Samplers for a Hierarchical Random Effects Model , 1998 .
[16] J. Booth,et al. Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm , 1999 .
[17] Jun S. Liu,et al. Monte Carlo EM with importance reweighting and its applications in random effects models 1 1 This wo , 1999 .
[18] Kenneth Lange,et al. Numerical analysis for statisticians , 1999 .
[19] Hoon Kim,et al. Monte Carlo Statistical Methods , 2000, Technometrics.
[20] Galin L. Jones,et al. Honest Exploration of Intractable Probability Distributions via Markov Chain Monte Carlo , 2001 .
[21] George Casella,et al. Implementations of the Monte Carlo EM Algorithm , 2001 .
[22] A. Agresti,et al. A Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses , 2001 .
[23] Wolfgang Jank,et al. A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model , 2001 .
[24] Brian D. Ripley,et al. geoRglm: A Package for Generalised Linear Spatial Models , 2002 .
[25] Brian S. Caffo,et al. Empirical supremum rejection sampling , 2002 .
[26] Galin L. Jones,et al. On the applicability of regenerative simulation in Markov chain Monte Carlo , 2002 .
[27] Jian Qing Shi,et al. Publication bias and meta‐analysis for 2×2 tables: an average Markov chain Monte Carlo EM algorithm , 2002 .
[28] Wolfgang Jank,et al. Efficiency of Monte Carlo EM and Simulated Maximum Likelihood in Two-Stage Hierarchical Models , 2003 .
[29] Karl J. Friston,et al. Variance Components , 2003 .
[30] Christian P. Robert,et al. Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.
[31] Lang Wu,et al. Generalized linear mixed models with informative dropouts and missing covariates , 2007 .
[32] P. Diggle,et al. Model‐based geostatistics , 2007 .