James P. Hobert

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Publications since 2008:

• 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. (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 Roman, 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.

• Marchev, D. and Hobert, J. P. (2009). Exact sampling from the Student's t model, Advances and Applications in Statistics, 13: 165-179.

• Tan, A. and Hobert, J. P. (2009). Block Gibbs sampling for Bayesian random effects models with improper priors: Convergence and regeneration, Journal of Computational and Graphical Statistics, 18: 861-878.

• Eaton, M. L., Hobert, J. P., Jones, G. L. and Lai, W.-L. (2008). Evaluation of formal posterior distributions via Markov chain arguments, Annals of Statistics, 36: 2423-2452.

• Hobert, J. P. and Marchev, D. (2008). A theoretical comparison of the data augmentation, marginal augmentation and PX-DA algorithms, Annals of Statistics, 36: 532-554.

• Booth, J. G., Casella, G. and Hobert, J. P. (2008). Clustering using objective functions and stochastic search, Journal of the Royal Statistical Society, Series B,70: 119-139.