Index: Robert B. Gramacy

I am teaching two sections of Applied Regression Analysis (BUS41100) in the Fall Quarter, 2012.

My research interests include Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimizaton under uncertainty. My application areas of interest include spatial data, sequential computer experiments, ecology, epidemiology, finance and public policy.

I am supervising the following students in the Statistical Laboratory at the University of Cambridge:

• James Lawrence (2008): Sequential Monte Carlo methods for model selection in ecology.
• Tim Graves (2009): Detection of long-range dependencies in time series from nature.

I participate in a cooperative statistical consulting effort with Scry Research.

See the full publication list, or my Google Scholar page.

• Local Gaussian process approximation for large computer experiments (2013) with Dan Apley; preprint on arXiv:1303.0383
• Estimating player contribution in hockey with regularized logistic regression (2013) with Shane Jensen, and Matt Taddy. Journal of Quantitative Analysis in Sports, 9(1), pp. 97-111; preprint on arXiv:1209.5026; also see our Capital Ideas article.
• Variable Selection and Sensitivity Analysis via Dynamic Trees with an Application to Computer Code Performance Tuning (2013) with Matt Taddy and Stefan Wild. Annals of Applied Statistics, 7(1), pp. 51-80; preprint on arXiv:1108.4739; also see our science highlight at Argonne
• Dynamic trees for streaming and massive data contexts (2012) with Christoforos Anagnostopoulos; preprint on arXiv:1201.5568
• Cases for the nugget in modeling computer experiments (2012) with Herbie Lee. Statistics and Computing, 22(3), pp. 713-722; preprint on arXiv:1007.4580