Research

I work on convergence analysis of Markov chains and the theory of Markov chain Monte Carlo (MCMC).

Articles

H. Li, Q. Qin and G. L. Jones (2022), Convergence analysis of data augmentation algorithms for Bayesian robust multivariate linear regression with incomplete data. arXiv.

Q. Qin and G. Wang (2022), Spectral telescope: Convergence rate bounds for random-scan Gibbs samplers based on a hierarchical structure. arXiv.

Q. Qin (2022), Analysis of two-component Gibbs samplers using the theory of two projections. arXiv.

Q. Qin and J. P. Hobert (2022), Geometric convergence bounds for Markov chains in Wasserstein distance based on generalized drift and contraction conditions, Annales de l’Institut Henri Poincaré (B) Probabilités et Statistiques. arXiv.

G. L. Jones and Q. Qin (2022), Markov chain Monte Carlo in Practice, Annual Review of Statistics and Its Application.

Q. Qin and G. L. Jones (2022), Convergence rates of two-component MCMC samplers, BernoulliarXiv.

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

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

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

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

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

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