James P. Hobert

• Recent Papers
• Official Department Page

Publications since 2007:

• Marchev, D. and Hobert, J. P. (2009). Exact sampling from the Student's t model, Advances and Applications in Statistics, to appear.

• 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, to appear.

• Hobert, J. P. (2009). The Data Augmentation Algorithm: Theory and Methodology, Handbook of Markov Chain Monte Carlo, to appear, S. Brooks, A. Gelman, G. Jones and X.-L. Meng, eds. Chapman & Hall/CRC Press.

• 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.

• Hobert, J. P. and Rosenthal, J. S. (2007). Norm comparisons for data augmentation, Advances and Applications in Statistics, 7: 291-302.

• Roy, V. and Hobert, J. P. (2007). Convergence rates and asymptotic standard errors for MCMC algorithms for Bayesian probit regression, Journal of the Royal Statistical Society, Series B, 69: 607-623.

• Hobert, J. P., Tan, A. and Liu, R. (2007). When is Eaton's Markov chain irreducible? Bernoulli, 13: 641-652.

• Eaton, M. L., Hobert, J. P. and Jones, G. L. (2007). On perturbations of strongly admissible prior distributions, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 43: 633-653.