James P. Hobert

• Recent Papers
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Publications since 2010:

• Román, J. C. and Hobert, J. P. (2012). Convergence analysis of the Gibbs sampler for Bayesian general linear mixed models with improper priors, Annals of Statistics (to appear).

• Tan, A., Jones, G. L. and Hobert, J. P. (2012). On the geometric ergodicity of two-variable Gibbs samplers, Festschrift for Morris Eaton (to appear).

• Khare, K. and Hobert, J. P. (2012). Geometric ergodicity of the Gibbs sampler for Bayesian quantile regression, Journal of Multivariate Analysis, 112:108-116.

• Khare, K. and Hobert, J. P. (2011). A spectral analytic comparison of trace-class data augmentation algorithms and their sandwich variants, Annals of Statistics, 39: 2585-2606.

• Hobert, J. P., Roy, V. and Robert, C. P. (2011). Improving the convergence properties of the data augmentation algorithm with an application to Bayesian mixture modelling, Statistical Science, 26: 332-351.

• Hobert, J. P. and Román, J. C. (2011). Discussion of ``To center or not to center: That is not the question - An ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC efficiency,'' by Y. Yu and X.-L. Meng, to appear in Journal of Computational and Graphical Statistics, 20: 571-580.

• Hobert, J. P. (2011). The Data Augmentation Algorithm: Theory and Methodology, Handbook of Markov Chain Monte Carlo, S. Brooks, A. Gelman, G. Jones and X.-L. Meng, eds. Chapman & Hall/CRC Press.

• Doss, H. and Hobert, J. P. (2010). Estimation of Bayes factors in a class of hierarchical random effects models using a geometrically ergodic MCMC algorithm, Journal of Computational and Graphical Statistics, 19: 295-312.

• Joo, Y., Casella, G. and Hobert, J. P. (2010). Bayesian model-based tight clustering for time course data, Computational Statistics, 25: 17-38.

• Roy, V. and Hobert, J. P. (2010). On Monte Carlo methods for Bayesian multivariate regression models with heavy-tailed errors, Journal of Multivariate Analysis, 101: 1190-1202.