Research Interests


My main area of research interest is the development of Bayesian methodology for the analysis of modern, complex datasets. I am particularly interested in the development of computational methods to address these problems. Particular areas of interest include Markov chain Monte Carlo methods as well as the use of parallel computing in statistics.

Specific methodological research areas include the problems of variable selection and model uncertainty in contexts of regression, prediction and complex multivariate modeling with many variables. A key element of this research is the development of stochastic search and MCMC methods for exploring large model spaces. Recent focus has been on the development of new classes of prior distributions for regression problems that provide connections to penalized optimization procedures.

You can find information about Bayesian activities and research across the world at www.bayesian.org.


Research Activities


Papers and Publications

  1. Casey B. Davis, Christopher M. Hans and Thomas J. Santner (2019). Prediction using a Bayesian heteroscedastic composite Gaussian process. arXiv:1906.10737

  2. Agniva Som, Christopher M. Hans and Steven N. MacEachern (2016). A conditional Lindley paradox in Bayesian linear models. Biometrika, 103, 993-999. doi: 10.1093/biomet/asw037.

  3. Christopher M. Hans (2016). Comment on article by Pratola. (Invited discussion of "Efficient Metropolis–Hastings Proposal Mechanisms for Bayesian Regression Tree Models" by Matthew T. Pratola) Bayesian Analysis, 11, 921-927.

  4. Christopher M. Hans and Mario Peruggia (2015). Comment on Article by Dawid and Musio. (Invited discussion of "Bayesian Model Selection Based on Proper Scoring Rules" by A. Philip Dawid and Monica Musio) Bayesian Analysis, 10, 505-509.

  5. Agniva Som, Christopher M. Hans and Steven N. MacEachern (2014). Block Hyper-g Priors in Bayesian Regression. arXiv:1406.6419

  6. Chris Hans, Greg M. Allenby, Peter F. Craigmile, Juhee Lee, Steven N. MacEachern and Xinyi Xu (2012). Covariance Decompositions for Accurate Computation in Bayesian Scale-Usage Models. Journal of Computational and Graphical Statistics, 21, 538-557.

  7. Chris Hans (2011). Elastic Net Regression Modeling With the Orthant Normal Prior. Journal of the American Statistical Association, 106, 1383-1393.

  8. Chris Hans (2011). Comment on article by Polson and Scott. Invited discussion of Data Augmentation for Support Vector Machines by Nicholas G. Polson and Steven L. Scott. Bayesian Analysis, 6, 37-42.

  9. Chris Hans (2011). Discussion of "Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction" by Nicholas G. Polson and James G. Scott. In Bayesian Statistics 9, eds. J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West. Oxford, U. K.: Oxford University Press, 530-531.

  10. Chris Hans (2010). Model uncertainty and variable selection in Bayesian lasso regression. Statistics and Computing, 20, 221-229.

  11. Chris Hans. Bayesian lasso regression. (2009) Biometrika, 96, 835-845.

  12. Chris Hans, Adrian Dobra and Mike West (2007). Shotgun stochastic search for "large p" regression. Journal of the American Statistical Association, 102, 507-516.

  13. H.K. Dressman, C. Hans, A. Bild, J. Olson, E. Rosen, P.K. Marcom, V. Liotcheva, E. Jones, Z. Vujaskovic, J. Marks, M.W. Dewhirst, M. West, J.R. Nevins and K. Blackwell (2006). Gene expression profiles of multiple breast cancer phenotypes and response to neoadjuvant chemotherapy. Clinical Cancer Research, 12, 819-826

  14. Chris Hans and David B. Dunson (2005). Bayesian Inferences on Umbrella Orderings. Biometrics, 61, 1018-1026.

  15. Beatrix Jones, Carlos Carvalho, Adrian Dobra, Chris Hans, Chris Carter and Mike West (2005). Experiments in stochastic computation for high-dimensional graphical models. Statistical Science, 20, 388-400.

  16. Jeremy N. Rich, Chris Hans, Beatrix Jones, Edwin S. Iversen, Roger E. McClendon, B.K. Ahmed Rasheed, Adrian Dobra, Holly K. Dressman, Darell D. Bigner, Joseph R. Nevins and Mike West (2005). Gene expression profiling and genetic markers in glioblastoma survival. Cancer Research, 65, 4051-4058.

  17. Adrian Dobra, Chris Hans, Beatrix Jones, Joseph R. Nevins, Guang Yao and Mike West (2004). Sparse graphical models for exploring gene expression data. Journal of Multivariate Analysis, 90, 196-212.

  18. David A. van Dyk and Christopher M. Hans (2002). Accounting for absorption lines in images obtained with the Chandra X-ray observatory, in Spatial Cluster Modelling (Eds. A. Lawson and D. Denison), Chapman and Hall/CRC, 175-198.

Grants

Software

Ph.D. Students