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2020 – today
- 2020
[j8]Grant Backlund
, James P. Hobert:
A note on the convergence rate of MCMC for robust Bayesian multivariate linear regression with proper priors. Comput. Math. Methods 2(3) (2020)
2010 – 2019
- 2019
- [j7]Tavis Abrahamsen, James P. Hobert:
Fast Monte Carlo Markov chains for Bayesian shrinkage models with random effects. J. Multivar. Anal. 169: 61-80 (2019) - 2018
- [j6]Qian Qin, James P. Hobert:
Trace-class Monte Carlo Markov chains for Bayesian multivariate linear regression with non-Gaussian errors. J. Multivar. Anal. 166: 335-345 (2018) - 2013
- [j5]Hee Min Choi, James P. Hobert:
Analysis of MCMC algorithms for Bayesian linear regression with Laplace errors. J. Multivar. Anal. 117: 32-40 (2013) - 2012
- [j4]Kshitij Khare, James P. Hobert:
Geometric ergodicity of the Gibbs sampler for Bayesian quantile regression. J. Multivar. Anal. 112: 108-116 (2012) - 2010
- [j3]Yongsung Joo, George Casella, James P. Hobert:
Bayesian model-based tight clustering for time course data. Comput. Stat. 25(1): 17-38 (2010) - [j2]Vivekananda Roy, James P. Hobert:
On Monte Carlo methods for Bayesian multivariate regression models with heavy-tailed errors. J. Multivar. Anal. 101(5): 1190-1202 (2010)
1990 – 1999
- 1999
- [j1]James P. Hobert, Christian P. Robert, D. M. Titterington:
On perfect simulation for some mixtures of distributions. Stat. Comput. 9(4): 287-298 (1999)
Coauthor Index
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