| Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm JG Booth, JP Hobert Journal of the Royal Statistical Society Series B: Statistical Methodology …, 1999 | 803 | 1999 |
| The effect of improper priors on Gibbs sampling in hierarchical linear mixed models JP Hobert, G Casella Journal of the American Statistical Association 91 (436), 1461-1473, 1996 | 653 | 1996 |
| Honest exploration of intractable probability distributions via Markov chain Monte Carlo GL Jones, JP Hobert Statistical Science, 312-334, 2001 | 368 | 2001 |
| Random‐effects modeling of categorical response data A Agresti, JG Booth*, JP Hobert*, B Caffo* Sociological Methodology 30 (1), 27-80, 2000 | 272 | 2000 |
| Standard errors of prediction in generalized linear mixed models JG Booth, JP Hobert Journal of the American Statistical Association 93 (441), 262-272, 1998 | 245 | 1998 |
| Negative binomial loglinear mixed models JG Booth, G Casella, H Friedl, JP Hobert Statistical Modelling 3 (3), 179-191, 2003 | 180 | 2003 |
| On the applicability of regenerative simulation in Markov chain Monte Carlo JP Hobert, GL Jones, B Presnell, JS Rosenthal Biometrika 89 (4), 731-743, 2002 | 155 | 2002 |
| Sufficient burn-in for Gibbs samplers for a hierarchical random effects model GL Jones, JP Hobert | 148 | 2004 |
| Clustering using objective functions and stochastic search JG Booth, G Casella, JP Hobert Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2008 | 147 | 2008 |
| The Polya-Gamma Gibbs sampler for Bayesian logistic regression is uniformly ergodic HM Choi, JP Hobert | 119 | 2013 |
| Convergence rates and asymptotic standard errors for Markov chain Monte Carlo algorithms for Bayesian probit regression V Roy, JP Hobert Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2007 | 104 | 2007 |
| Geometric ergodicity of Gibbs and block Gibbs samplers for a hierarchical random effects model JP Hobert, CJ Geyer Journal of Multivariate Analysis 67 (2), 414-430, 1998 | 104 | 1998 |
| A theoretical comparison of the data augmentation, marginal augmentation and PX-DA algorithms JP Hobert, D Marchev | 98 | 2008 |
| Functional compatibility, Markov chains, and Gibbs sampling with improper posteriors JP Hobert, G Casella Journal of Computational and Graphical Statistics 7 (1), 42-60, 1998 | 85 | 1998 |
| A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model JG Booth, JP Hobert, W Jank Statistical Modelling 1 (4), 333-349, 2001 | 74 | 2001 |
| The data augmentation algorithm: Theory and methodology JP Hobert Handbook of Markov Chain Monte Carlo, 253-293, 2011 | 68 | 2011 |
| Geometric ergodicity of the Bayesian lasso K Khare, JP Hobert | 58 | 2013 |
| Geometric Ergodicity of van Dyk and Meng's Algorithm for the Multivariate Student's t Model D Marchev, JP Hobert Journal of the American Statistical Association 99 (465), 228-238, 2004 | 57 | 2004 |
| Block Gibbs sampling for Bayesian random effects models with improper priors: Convergence and regeneration A Tan, JP Hobert Journal of Computational and Graphical Statistics 18 (4), 861-878, 2009 | 52 | 2009 |
| Geometric ergodicity of the Gibbs sampler for Bayesian quantile regression K Khare, JP Hobert Journal of Multivariate Analysis 112, 108-116, 2012 | 50 | 2012 |
Galin JonesSchool of Statistics, University of MinnesotaEmail yang diverifikasi di umn.edu
