Research Interests: applications in bioinformatics, bayesian multi-level modeling, statistical computing and mcmc methods, statistics in sports
PhD, Harvard University, 2004
AM, Harvard University, 2001
MS, McGill University, 1999
BS, McGill University, 1997
Leonard J. Savage Award for best thesis in Application Methodology from the International Society for Bayesian Analysis (2005)
David W. Hauck Award for Outstanding Teaching (2009)
Sports in Statistics Award for Contributions to the Statistics in Sports Community, American Statistical Association (2011)
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Cecilia Balocchi and Shane T. Jensen (2019), Spatial modeling of trends in crime over time in Philadelphia, Annals of Applied Statistics, 13 (4), pp. 2235-2259.
Cecilia Balocchi, Sameer K. Deshpande, Edward I. George, Shane T. Jensen (Under Review), Crime in Philadelphia: Bayesian Clustering with Particle Optimization.
Jose Marcio Luna, Efstathios D. Gennatas, Lyle H. Ungar, Eric Eaton, Eric S. Diffenderfer, Shane T. Jensen, Charles B. Simone II, Jerome H. Friedman, Timothy D. Solberg, Gilmer Valdes (2019), Building more accurate decision trees with the additive tree, Proceedings of the National Academy of Sciences of the United States of America, 116 (40), pp. 19887-19893.
Colman Humphrey, Dylan Small, Shane T. Jensen, Kevin Volpp, David A. Asch, Jingsan Zhu, Andrea B. Troxel (2019), Modeling Lottery Incentives for Daily Adherence, Statistics in Medicine, 38 (15), pp. 2847-2867.
Jason Mulholland and Shane T. Jensen (2019), Optimizing the allocation of funds of an NFL team under the salary cap, International Journal of Forecasting, 35 (2), pp. 767-775.
Namita Nandakumar and Shane T. Jensen (2019), Historical Perspectives and Current Directions in Hockey Analytics, Annual Review of Statistics and Its Applications, 6, pp. 19-36.
Gilmer Valdes, Albert J. Chang, Yannet Interian, Kenton Owens, Shane T. Jensen, Lyle Ungar, Adam Cunha, Timothy D. Solberg, I-Chow Hsu (2018), Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis, International Journal of Radiation Oncology - Biology - Physics, 101 (3), pp. 694-703.
Mulholland, J. and Shane T. Jensen (2018), Predicting the future of free agent receivers and tight ends in the NFL, Statistica Applicata - Italian Journal of Applied Statistics, 30 (2), pp. 269-294.
The Senior Capstone Project is required for all BAS degree students, in lieu of the senior design course. The Capstone Project provides an opportunity for the student to apply the theoretical ideas and tools learned from other courses. The project is usually applied, rather than theoretical, exercise, and should focus on a real world problem related to the career goals of the student. The one-semester project may be completed in either the fall or sprong term of the senior year, and must be done under the supervision of a sponsoring faculty member. To register for this course, the student must submit a detailed proposal, signed by the supervising professor, and the student's faculty advisor, to the Office of Academic Programs two weeks prior to the start of the term.
Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.
Introduction to concepts in probability. Basic statistical inference procedures of estimation, confidence intervals and hypothesis testing directed towards applications in science and medicine. The use of the JMP statistical package.
The course will introduce data analysis from the Bayesian perspective to undergraduate students. We will cover important concepts in Bayesian probability modeling as well as estimation using both optimization and simulation-based strategies. Key topics covered in the course include hierarchical models, mixture models, hidden Markov models and Markov Chain Monte Carlo.
Sophisticated tools for probability modeling and data analysis from the Bayesian perspective. Hierarchical models, mixture models and Monte Carlo simulation techniques.
This course provides the fundamental methods of statistical analysis, the art and science if extracting information from data. The course will begin with a focus on the basic elements of exploratory data analysis, probability theory and statistical inference. With this as a foundation, it will proceed to explore the use of the key statistical methodology known as regression analysis for solving business problems, such as the prediction of future sales and the response of the market to price changes. The use of regression diagnostics and various graphical displays supplement the basic numerical summaries and provides insight into the validity of the models. Specific important topics covered include least squares estimation, residuals and outliers, tests and confidence intervals, correlation and autocorrelation, collinearity, and randomization. The presentation relies upon computer software for most of the needed calculations, and the resulting style focuses on construction of models, interpretation of results, and critical evaluation of assumptions.
This graduate course will cover the modeling and computation required to perform advanced data analysis from the Bayesian perspective. We will cover fundamental topics in Bayesian probability modeling and implementation, including recent advances in both optimization and simulation-based estimation strategies. Key topics covered in the course include hierarchical and mixture models, Markov Chain Monte Carlo, hidden Markov and dynamic linear models, tree models, Gaussian processes and nonparametric Bayesian strategies.
for the paper “OpenWAR: an open source system for evaluating overall player performance in major league baseball.”
Recent Wharton research examines how healthy energy in a particular neighborhood can help reduce crime.Knowledge @ Wharton - 2017/06/12