James P. Hobert
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.