Markov Chain Monte Carlo for Linear Mixed Models
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[1] G. C. Wei,et al. A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms , 1990 .
[2] J. Rosenthal,et al. General state space Markov chains and MCMC algorithms , 2004, math/0404033.
[3] Gareth O. Roberts,et al. Convergence of Conditional Metropolis-Hastings Samplers , 2014, Advances in Applied Probability.
[4] Brian S. Caffo,et al. Ascent-Based Monte Carlo EM , 2003 .
[5] Hee Min Choi,et al. The Polya-Gamma Gibbs sampler for Bayesian logistic regression is uniformly ergodic , 2013 .
[6] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[7] Gersende Fort,et al. Convergence of the Monte Carlo expectation maximization for curved exponential families , 2003 .
[8] Scott L. Zeger,et al. Generalized linear models with random e ects: a Gibbs sampling approach , 1991 .
[9] New York Dover,et al. ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHM , 1983 .
[10] Brady T. West,et al. Linear Mixed Models: A Practical Guide Using Statistical Software , 2006 .
[11] Charles J. Geyer,et al. Likelihood and Exponential Families , 1990 .
[12] N. Breslow,et al. Approximate inference in generalized linear mixed models , 1993 .
[13] J. Booth,et al. Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm , 1999 .
[14] T. Louis. Finding the Observed Information Matrix When Using the EM Algorithm , 1982 .
[15] Murray Aitkin,et al. Variance Component Models with Binary Response: Interviewer Variability , 1985 .
[16] Richard L. Tweedie,et al. Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.
[17] P. Thall,et al. Some covariance models for longitudinal count data with overdispersion. , 1990, Biometrics.
[18] G. Casella,et al. The Bayesian Lasso , 2008 .
[19] Michael Lavine,et al. The Multiset Sampler , 2009 .
[20] A. Desideri,et al. Modeling healthcare costs in simultaneous presence of asymmetry, heteroscedasticity and correlation , 2013 .
[21] James S. Hodges,et al. Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects , 2013 .
[22] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[23] Gareth O. Roberts,et al. Markov Chains and De‐initializing Processes , 2001 .
[24] C. McCulloch,et al. Generalized Linear Mixed Models , 2005 .
[25] S. F. Jarner,et al. Geometric ergodicity of Metropolis algorithms , 2000 .
[26] C. McCulloch. Maximum Likelihood Variance Components Estimation for Binary Data , 1994 .
[27] C. McCulloch. Maximum Likelihood Algorithms for Generalized Linear Mixed Models , 1997 .
[28] Galin L. Jones,et al. Honest Exploration of Intractable Probability Distributions via Markov Chain Monte Carlo , 2001 .
[29] Galin L. Jones. On the Markov chain central limit theorem , 2004, math/0409112.
[30] Galin L. Jones,et al. Fixed-Width Output Analysis for Markov Chain Monte Carlo , 2006, math/0601446.
[31] Roderick J. A. Little,et al. Statistical Analysis with Missing Data , 1988 .
[32] R. Tweedie,et al. Rates of convergence of the Hastings and Metropolis algorithms , 1996 .
[33] C. Geyer,et al. Correction: Variable transformation to obtain geometric ergodicity in the random-walk Metropolis algorithm , 2012, 1302.6741.
[34] Dongchu Sun,et al. PROPRIETY OF POSTERIORS WITH IMPROPER PRIORS IN HIERARCHICAL LINEAR MIXED MODELS , 2001 .
[35] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[37] A. Agresti,et al. Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.
[38] J. Booth,et al. Standard Errors of Prediction in Generalized Linear Mixed Models , 1998 .
[39] G. Molenberghs,et al. Likelihood Ratio, Score, and Wald Tests in a Constrained Parameter Space , 2007 .
[40] Charles R. Johnson,et al. Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.
[41] James P. Hobert,et al. The Data Augmentation Algorithm: Theory and Methodology , 2011 .
[42] B. Efron,et al. Data Analysis Using Stein's Estimator and its Generalizations , 1975 .
[43] R. Cogburn,et al. The central limit theorem for Markov processes , 1972 .
[44] A. Agresti,et al. A Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses , 2001 .
[45] C. Geyer. On the Convergence of Monte Carlo Maximum Likelihood Calculations , 1994 .
[46] P. McCullagh,et al. Generalized Linear Models , 1972, Predictive Analytics.
[47] E. L. Lehmann,et al. Theory of point estimation , 1950 .
[48] R. Wolfinger,et al. Generalized linear mixed models a pseudo-likelihood approach , 1993 .
[49] R. Tweedie,et al. Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms , 1996 .
[50] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[51] G. McLachlan,et al. The EM algorithm and extensions , 1996 .
[52] E. Egelman,et al. Composition and mass of the bacteriophage phi29 prohead and virion. , 2001, Journal of structural biology.
[53] Herwig Friedl,et al. Negative binomial loglinear mixed models , 2003 .
[54] K. Chan,et al. Monte Carlo EM Estimation for Time Series Models Involving Counts , 1995 .