Papers and Talks


Qin and Hobert (2019). Geometric convergence bounds for Markov chains in Wasserstein distance based on generalized drift and contraction conditions. arXiv: 1902.02964.

Qin and Hobert (2018). Wasserstein-based methods for convergence complexity analysis of MCMC with application to Albert and Chib’s algorithm. arXiv: 1810.08826.

Qin, Hobert and Khare (2019). Estimating the spectral gap of a trace-class Markov operator. Electronic Journal of Statistics, to appear. arXiv: 1704.00850.

Qin and Hobert (2019). Convergence complexity analysis of Albert and Chib’s algorithm for Bayesian probit regression. Annals of Statistics, to appear. arXiv:¬†1712.08867.

Hobert, Jung, Khare and Qin (2018). Convergence analysis of MCMC algorithms for Bayesian multivariate linear regression with non-Gaussian errors. Scandinavian Journal of Statistics. arXiv: 1506.03113.

Qin and Hobert (2018). Trace-class Monte Carlo Markov Chains for Bayesian Multivariate Linear Regression with Non-Gaussian Errors. Journal of Multivariate Analysis. arXiv: 1602.00136.


“Convergence complexity analysis of Albert and Chib’s algorithm”. Joint Statistical Meetings, Vancouver, Aug 1, 2018.

“Estimating the spectral gap of a trace-class Markov operator”. LMS-EPSRC Durham Symposium, Durham University, July 29, 2017.