James P. Hobert

• Recent Papers

Publications since 2015:

• Jin, Z. and Hobert, J. P. (2022). On the convergence rate of the "out-of-order" block Gibbs sampler, Statistics & Probability Letters, 188.

• Qin, Q. and Hobert, J. P. (2022). Geometric convergence bounds for Markov chains in Wasserstein distance based on generalized drift and contraction conditions, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 58: 872-889.

• Jin, Z. and Hobert, J. P. (2022). Dimension free convergence rates for Gibbs samplers for Bayesian linear mixed models, Stochastic Processes and their Applications, 148: 25-67.

• Qin, Q. and Hobert, J. P. (2022). Wasserstein-based methods for convergence complexity analysis of MCMC with applications, Annals of Applied Probability, 32: 124-166.

• Davis, B. and Hobert, J. P. (2021). On the convergence complexity of Gibbs samplers for a family of simple Bayesian random effects models, Methodology & Computing in Applied Probability, 23: 1323-1351.

• Qin, Q. and Hobert, J. P. (2021). On the limitations of single-step drift and minorization in Markov chain convergence analysis, Annals of Applied Probability, 31: 1633-1659.

• Backlund, G., Hobert, J. P., Jung, Y. J. and Khare, K. (2021). A hybrid scan Gibbs sampler for Bayesian models with latent variables, Statistical Science, 36: 379-399.

• Backlund, G. and Hobert, J. P. (2020). A note on the convergence rate of MCMC for robust Bayesian multivariate linear regression with proper priors, Computational and Mathematical Methods, 2.

• Qin, Q., Hobert, J. P. and Khare, K. (2019). Estimating the spectral gap of a trace-class Markov operator, Electronic Journal of Statistics, 13: 1790-1822.

• Qin, Q. and Hobert, J. P. (2019). Convergence complexity analysis of Albert and Chib's algorithm for Bayesian probit regression, Annals of Statistics, 47: 2320-2347.

• Abrahamsen, T. and Hobert, J. P. (2019). Fast Monte Carlo Markov chains for Bayesian shrinkage models with random effects, Journal of Multivariate Analysis, 169: 61-80.

• Hobert, J. P., Jung, Y. J., Khare, K. and Qin, Q. (2018). Convergence analysis of MCMC algorithms for Bayesian multivariate linear regression with non-Gaussian errors, Scandinavian Journal of Statistics, 45: 513-533.

• Qin, Q. and Hobert, J. P. (2018). Trace-class Monte Carlo Markov chains for Bayesian multivariate linear regression with non-Gaussian errors, Journal of Multivariate Analysis, 166: 335-345.

• 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.