## James P. Hobert

## Teaching

## Research

### Submissions:

- Qin and
Hobert (2019). Geometric convergence bounds for Markov chains in
Wasserstein distance based on generalized drift and contraction
conditions. arXiv

- Qin
and Hobert (2019). Wasserstein-based methods for convergence
complexity analysis of MCMC with
applications. arXiv

### Accepted papers that have yet to appear:

- Qin and Hobert (2021+). On
the limitations of single-step drift and minorization in Markov
chain convergence analysis,
* Annals of Applied
Probability* arXiv

- Davis and Hobert (2021+). On the convergence complexity of Gibbs
samplers for a family of simple Bayesian random effects
models,
* Methodology & Computing in Applied
Probability* arXiv

- Backlund,
Hobert, Jung
and Khare
(2021+). A hybrid scan Gibbs sampler for Bayesian models with
latent variables,
* Statistical
Science* arXiv

### Papers that have recently appeared:

- Backlund
and Hobert (2020). A note on the convergence rate of MCMC for
robust Bayesian multivariate linear regression with proper
priors,
* Computational and Mathematical
Methods* pdf

- Qin, Hobert
and Khare
(2019). Estimating the spectral gap of a trace-class Markov
operator,
* Electronic Journal of
Statistics* arXiv

- Qin and
Hobert (2019). Convergence complexity analysis of Albert and
Chib's algorithm for Bayesian probit regression,
* Annals of
Statistics* arXiv

- Abrahamsen and Hobert (2019). Fast Monte Carlo Markov chains for
Bayesian shrinkage models with random effects,
* Journal of
Multivariate
Analysis* pdf