Research Interests
Markov chain Monte Carlo, non/semi-parametric Bayesian methods,
Bayesian methods in biostatistics.
Selected Papers Since 2005
- Burr, D., Doss, H. (2005). A Bayesian semi-parametric model
for random effects meta-analysis.
[PostScript]
[PDF] (This has appeared in Journal of
the American Statistical Association, 100 242-251.)
- Doss, H. (2007). Some thoughts on future directions in
Bayesian model selection.
[PostScript]
[PDF] (This has appeared in Statistica
Sinica, 17 413-421.)
- Doss, H. (2008). Comment: Quantifying information loss in
survival studies. (This is a discussion of "Quantifying the
fraction of missing information for hypothesis testing in
statistical and genetic studies" by D. Nicolae, X.-L. Meng, and
A. Kong.) [PostScript]
[PDF]. (This has
appeared in Statistical Science, 23 313-317.)
- Doss, H., and Hobert, J.P. (2010). Estimation of Bayes
factors in a class of hierarchical random effects models using a
geometrically ergodic MCMC algorithm.
[PostScript]
[PDF]. (This has appeared in Journal
of Computational and Graphical Statistics, 19
295-312.)
- Doss, H. (2010). Estimation of large families of Bayes
factors from Markov chain output.
[PostScript]
[PDF]. (This has appeared in Statistica
Sinica, 20 537-560.)
- Buta, E. and Doss, H. (2011). Computational approaches for
empirical Bayes methods and Bayesian sensitivity
analysis.
[PostScript]
[PDF]. (This has appeared in The Annals of
Statistics, 39 2658-2685.)
- Doss, Hani (2012). Hyperparameter and model selection for
nonparametric Bayes problems via Radon-Nikodym
derivatives.
[PostScript]
[PDF]. (This has appeared in Statistica
Sinica, 22 1-26.)
- Doss, H. and Tan, A. (2014). Estimates and standard errors
for ratios of normalizing constants from multiple Markov chains
via regeneration.
[PostScript]
[PDF]. (This has appeared in
Journal of the Royal Statistical Society, Series B,
76 683-712.)
- Tan, A., Doss, H. and Hobert, J.P. (2015). Honest importance
sampling with multiple Markov
chains.
[PostScript]
[PDF]. (This has appeared in
Journal of Computational and Graphical Statistics,
24 792-826.)
- Doss, H. and Park, Y. (2017). An MCMC approach to empirical
Bayes inference and Bayesian sensitivity analysis via empirical
processes.
[PostScript]
[PDF]. (This will appear in
The Annals of Statistics.)
- George, C.P. and Doss, H. (2017). Principled selection of
hyperparameters in the latent Dirichlet allocation model.
[Main Paper
PostScript]
[Supplement PostScript]
[Main Paper PDF]
[Supplement PDF].
(Under Minor Revision for
Journal of Machine Learning Research.)
Software
- bsa Software
for dynamic visualization of changing prior and posterior in
Bayesian nonparametric analysis of censored data. This is joint
work with B. Narasimhan.
- mc-mdp-surv
Software for Monte Carlo methods for Bayesian analysis of
survival data using mixtures of Dirichlet process priors
(JCGS 2003, 282-307). This is joint work with Fred
Huffer.