- Hobert, J. P., Jung, Y. J., Khare, K. and Qin,
Q. (2017). Convergence analysis of MCMC algorithms for Bayesian
multivariate linear regression with non-Gaussian errors,
*Scandinavian Journal of Statistics*, to appear. - Pal, S., Khare, K. and Hobert, J. P. (2017). Trace class Markov
chains for Bayesian inference with generalized double Pareto
shrinkage priors,
*Scandinavian Journal of Statistics*,**44**: 307-323. - Abrahamsen, T. and Hobert, J. P. (2017). Convergence analysis of
block Gibbs samplers for Bayesian linear mixed models with p >
N,
*Bernoulli*,**23**: 459-478. - Hobert, J. P. and Khare, K. (2016). Discussion of "Posterior
inference in Bayesian quantile regression with asymmetric
Laplace likelihood," by Yang, Wang and He,
*International Statistical Review*,**84**: 349-356. - Choi, H. M. and Hobert, J. P. (2016). A comparison theorem for
data augmentation algorithms with applications,
*Electronic Journal of Statistics*,**10**: 308-329. - Pal, S., Khare, K. and Hobert, J. P. (2015). Improving the DA
algorithm in the two-block setup,
*Journal of Computational and Graphical Statistics*,**24**: 1114-1133. - Hobert, J. P. and Khare, K. (2015). Computable upper bounds on
the distance to stationarity for Jovanovski and Madras's Gibbs
sampler,
*Annales de la Faculte des Sciences de Toulouse*,**24**: 935-947. - Tan, A., Doss, H. and Hobert, J. P. (2015). Honest importance
sampling with multiple Markov chains,
*Journal of Computational and Graphical Statistics*,**24**: 792-826. - Román, J. C. and Hobert, J. P. (2015). Geometric ergodicity
of Gibbs samplers for Bayesian general linear mixed models with
proper priors,
*Linear Algebra and its Applications*,**473**: 54-77. - Jung, Y. J. and Hobert, J. P. (2014). Spectral properties of
MCMC algorithms for Bayesian linear regression with generalized
hyperbolic errors,
*Statistics & Probability Letters*,**95**: 92-100. - Román, J. C., Hobert, J. P. and Presnell, B. (2014). On
reparametrization and the Gibbs sampler,
*Statistics & Probability Letters*,**91**: 110-116. - Khare, K. and Hobert, J. P. (2013). Geometric ergodicity of the
Bayesian lasso,
*Electronic Journal of Statistics*,**7**: 2150-2163. - Choi, H. M. and Hobert, J. P. (2013). The Polya-Gamma Gibbs
sampler for Bayesian logistic regression is uniformly ergodic,
*Electronic Journal of Statistics*,**7**: 2054-2064. - Choi, H. M. and Hobert, J. P. (2013). Analysis of MCMC
algorithms for Bayesian linear regression with Laplace errors,
*Journal of Multivariate Analysis*,**117**: 32-40. - Tan, A., Jones, G. L. and Hobert, J. P. (2013). On the geometric
ergodicity of two-variable Gibbs samplers. In
*Advances in Modern Statistical Theory and Applications: A Festschrift in Honor of Morris L. Eaton*(G. L. Jones and X. Shen, eds.) 25-42.*IMS Collections Ser.***10**. IMS, Beachwood, OH. - 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*,**40**: 2823-2849. - Khare, K. and Hobert, J. P. (2012). Geometric ergodicity of the
Gibbs sampler for Bayesian quantile regression,
*Journal of Multivariate Analysis*,**112**:108-116.