James P. Hobert
James P. Hobert
Professor of Statistics, University of Florida
Verified email at ufl.edu
Title
Cited by
Cited by
Year
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
7121999
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
5961996
Honest exploration of intractable probability distributions via Markov chain Monte Carlo
GL Jones, JP Hobert
Statistical Science, 312-334, 2001
3132001
Random‐effects modeling of categorical response data
A Agresti, JG Booth*, JP Hobert*, B Caffo*
Sociological Methodology 30 (1), 27-80, 2000
2502000
Standard errors of prediction in generalized linear mixed models
JG Booth, JP Hobert
Journal of the American Statistical Association 93 (441), 262-272, 1998
2161998
Negative binomial loglinear mixed models
JG Booth, G Casella, H Friedl, JP Hobert
Statistical Modelling 3 (3), 179-191, 2003
1432003
Clustering using objective functions and stochastic search
JG Booth, G Casella, JP Hobert
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2008
1362008
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
1332002
Sufficient burn-in for Gibbs samplers for a hierarchical random effects model
GL Jones, JP Hobert
The Annals of Statistics 32 (2), 784-817, 2004
1322004
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
911998
The Polya-Gamma Gibbs sampler for Bayesian logistic regression is uniformly ergodic
HM Choi, JP Hobert
Electronic Journal of Statistics 7, 2054-2064, 2013
892013
A theoretical comparison of the data augmentation, marginal augmentation and PX-DA algorithms
JP Hobert, D Marchev
The Annals of Statistics 36 (2), 532-554, 2008
862008
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
862007
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
761998
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
692001
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
522004
The data augmentation algorithm: Theory and methodology
JP Hobert
Handbook of Markov Chain Monte Carlo, 253-293, 2011
492011
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
472009
Hierarchical models: A current computational perspective
JP Hobert
Journal of the American Statistical Association 95 (452), 1312-1316, 2000
412000
Geometric ergodicity of the Bayesian lasso
K Khare, JP Hobert
Electronic Journal of Statistics 7, 2150-2163, 2013
402013
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