My research is directed at methodology in statistics, econometrics, and machine learning, and
applications in business, social science, and engineering.
Big Data and Bayesian nonparametrics,
Segregation in HD,
Inversion of distributed langage representations
Semi-parametric inference for the means of heavy-tailed distributions with Lopes and Gardner.
Measuring polarization in high dimensional data, with Gentzkow and Shapiro.
Document classification by inversion of distributed language
representations. Proceedings of the 53rd meeting of the Association for Computational Linquistics (ACL 2015). gensim demo.
Causal inference in repeated observational studies: A case study of eBay product releases with Von Brzeski and Draper.
Bayesian and empirical Bayesian forests with Chen, Yu, and Wyle. Proceedings of the 32nd International Conference on Machine Learning (ICML 2015). pyspark demo.
Distributed multinomial regression.
Annals of Applied Statistics 9, 2015.
arXiv, distrom R package, +textir for use in MNIR.
One-step estimator paths for concave regularization.
gamlr R package.
A nonparametric Bayesian analysis of heterogeneous treatment effects in digital experimentation with Gardner, Chen, and Draper. To appear in the Journal of Business and Economic Statistics.
Hockey Player Performance via Regularized Logistic Regression with Gramacy and Tian. To appear in the Handbook of statistical methods for design and analysis in sports. slides
Multinomial inverse regression for text analysis, with discussion and
rejoinder. Journal of the American Statistical
Association 108, 2013.
textir R package,
Measuring political sentiment on Twitter:
factor-optimal design for multinomial inverse regression. Technometrics 55, 2013. arXiv
Contribution in Hockey with Regularized Logistic Regression, with
Gramacy and Jensen. Journal of Quantitative
Analysis of Sports 9, 2013.
piece, Chance article.
Variable Selection and
Sensitivity Analysis via Dynamic Trees with an Application to
Computer Code Performance Tuning, with Gramacy and Wild. Annals of Applied Statistics 7, 2013. arXiv).
Estimation and Selection for Topic Models. AISTATS 2012, JMLR
W&CP 22. maptpx R package, we8there.R example.
Mixture Modelling for
Marked Poisson Processes, with Kottas. Bayesian Analysis
Dynamic Trees for Learning and Design,
with Gramacy and Polson. Journal of the American Statistical Association 106, 2011. arXiv, dynaTree R package
An auto-regressive mixture model for dynamic spatial Poisson processes:
Application to tracking the intensity of violent crime. Journal of the American Statistical Association 105, 2010. local copy
Particle learning for general mixtures,
with Carvalho, Lopes, and Polson. Bayesian Analysis 5, 2010.
Bayesian nonparametric approach to inference for quantile
regression, with Kottas. Journal of Business and Economic
Statistics 28, 2010. (local copy)
Designing and anlayzing a circuit device experiment using treed Gaussian processes, with Lee, Gramacy, and Gray. A version of this appears as a chapter in the Handbook of Applied Bayesian Analysis, OUP 2010.
Categorical inputs, sensitivity analysis,
optimization and importance tempering with tgp version 2, with Gramacy. Journal of Statistical Software 33, 2010.
Selection of a representative sample,
with Lee and Gray. Journal of Classification 27,
Markov switching Dirichlet process mixture regression,
with Kottas. Bayesian Analysis 4, 2009.
Bayesian guided pattern search for robust local optimization,
with Lee, Gray, and Griffin. Technometrics 51, 2009. local copy
Fast inference for statistical inverse problems,
with Lee and Sansó. Inverse Problems 25, 2009. local copy
A statistical framework for the sensitivity analysis of radiative transfer models,
with Morris, Kottas, Furfaro, and Ganapol. IEEE Transactions on Geoscience and Remote Sensing 12, 2008. local copy
Thesis: Bayesian nonparametric
analysis of conditional distributions and
inference for Poisson point processes.