Professor of Statistics
"Wharton provides access to engage statistical problems in a wide variety of fields, including finance, marketing and public policy. Students have the opportunity to collaborate with world-class researchers in these fields."
Faculty Perspective
Faculty and Research
Faculty members in the Statistics Department have a wide range of active research interests. Particular areas of methodological research interest include nonparametric function estimation, high dimensional data, multiple hypothesis testing, bioinformatics, machine learning, observational studies, statistical graphics, causal inference, and time series analysis. Applications have included such areas as genetics, health outcomes, telephone call centers, baseball and credit card bankruptcy.
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Chairperson |
Associate Professors Assistant Professors
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Faculty with Secondary Appointments |
REPRESENTATIVE PUBLICATIONS
Names in bold type indicate authors who are current or former Wharton doctoral students. Names underlined are Wharton faculty members.
J. Abernethy and A. Rakhlin (2009), Beating the Adaptive Bandit with High Probability in Proceedings, Information Theory and Applications Workshop, COLT 2009.
Baiocchi, M., Small, D., Lorch, S. and Rosenbaum, P. Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants. Journal of the American Statistical Association, in press.
Brown, L. D., George, E., & Xu, X., (2008). Admissible Predictive Density Estimation, Annals of Statistics, 36, 1156-1171.
Cai, T., Low, M. & Zhao, L. (2007). Tradeoffs Between Global and Local Risks in Nonparametric Function Estimation. Bernoulli 13, 1-19.
Chen, L. and Buja, A. (2009), Local Multidimensional Scaling for Nonlinear Dimension Reduction, Graph Drawing and Proximity Analysis, Journal of the American Statistical Association, 104, 209-219.
Foster, D. and Stine, R. (2008), Alpha Investing: A Procedure for Sequential Control of Expected False Discoveries, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70, 429-444.
Chipman, H., George, E. and McCulloch, R. (2010), BART: Bayesian Additive Regression Trees, Annals of Applied Statistics, 2010, Vol. 4, No. 1, 266–298
Jensen, S.T., Shirley, K., and Wyner, A.J. (2009). Bayesball: a Bayesian Hierarchical Model for Evaluating Fielding in Major League Baseball, Annals of Applied Statistics 3:491-520.
Kakade, S., Sridharan, K and Tewari, A. (2009), On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds and Regularization, In Proceedings of NIPS, 2009.
Krieger, A.M., Pollak, M. and Samuel-Cahn, E. (2007), Select Sets: Rank and File, Annals of Applied Probability, 17, 360-385.
Shirley, K., Small, D., Lynch, K., Maisto, S. and Oslin, D. (2010), Hidden Markov Models for Alcoholism Treatment Trial Data. Annals of Applied Statistics, 4, 366-395.
Steele, M., The Cauchy-Schwarz Master Class: An Introduction to the Art of Mathematical Inequalities, Cambridge University Press and the Mathematical Association of America, Cambridge UK and Washington DC, 2004.
Weinberg, J., Brown, L.D. & Stroud, J.R. (2007). Bayesian Forecasting of an Inhomogeneous Poisson Process, with Applications to Call Center Data. Journal of the American Statistical Association, 102, 1185 -1198.
Wang, L., Brown, L. D., Cai, T. & Levine, M. (2008). Effect of Mean on Variance Function Estimation in Nonparametric Regression. The Annals of Statistics, 36, 646-664.
