Shane T. Jensen
Professor of Statistics
The Wharton School
University of Pennsylvania
463 Huntsman Hall, 3730 Walnut Street
stjensen at wharton.upenn.edu
|Recent Media Attention|
2019/08/20: Knowable Magazine article about our review of hockey analytics:
My research program is a collaborative and multidisciplinary effort that spans the fields of architecture, urban planning, criminology and statistics. Our endeavor is to take the available data on cities and set up artificial experimental situations that allow us to learn as objectively as possible about what aspects of city environments are associated with safety and other outcomes.
You can read more about our data collection and analysis pipeline in our paper:
Analysis of Urban Vibrancy and Safety in Philadelphia (2019) by C. Humphrey, S.T. Jensen, D. Small and R. Thurston.
and our recent efforts to model crime dynamics in Philadelphia in our paper:
Spatial modeling of trends in crime over time in Philadelphia (2019) by C. Balocchi and S.T. Jensen.
You can also read more about the goals of our urban analytics research program in these media articles:
Knowledge@Wharton: How Urban Planners Can Encourage Vibrancy — and Create Safer Cities.
Our urban analytics research program has received generous support from the Wharton Social Impact Initiative.
|Statistics in Sports|
If you are interested in sports and statistics, you should check out our weekly radio show Wharton Moneyball which is broadcast live 8-10am on Wednesdays on Wharton Business Radio:
Historical perspectives and current directions in hockey analytics (2019) by N. Nandakumar and S.T. Jensen. Annual Review of Statistics and Its Application 6:8.1-8.18.
Estimating an NBA player's impact on his team’s chances of winning (2016) by S. Deshpande and S.T. Jensen. Journal of Quantitative Analysis of Sports 12:51-72.
OpenWAR: an open source system for evaluating overall player performance in major league baseball (2015) by B.S. Baumer, G.J. Matthews and S.T. Jensen. Journal of Quantitative Analysis of Sports 11:69-84.
Predicting the Draft and Career Success of Tight Ends in the National Football League (2014) by J. Mulholland and S.T. Jensen. Journal of Quantitative Analysis of Sports 10:381-396.
Competing Process Hazard Function Models for Player Ratings in Ice Hockey (2013) by A.C. Thomas, S.L. Ventura, S.T. Jensen and S. Ma. Annals of Applied Statistics 7:1497-1524.
Estimating player contribution in hockey with regularized logistic regression (2013) by R.B. Gramacy, M. Taddy and S.T. Jensen Journal of Quantitative Analysis in Sports 9:97-111.
Hierarchical Bayesian modeling of hitting performance in baseball (2009) by S.T. Jensen, B. McShane and A.J. Wyner Bayesian Analysis 4:631-674.
Bayesball: a Bayesian hierarchical model for evaluating fielding in major league baseball (2009) by S.T. Jensen, K. Shirley and A.J. Wyner Annals of Applied Statistics 3:491-520.
SAFE: Spatial Aggregate Fielding Evaluation, our methodology for measuring fielding ability in major league baseball players using a hierarchical probit model. Results are presented across seven seasons of high-resolution ball-in-play data.
|Other Research Interests|
1. Genetics and Molecular Biology
Developing sophisticated statistical models for the evolution of genomic sequences. Areas of application include the response of HIV under various therapies as well as evolution during cancer progression. Developing models for combining heterogeneous data sources to refine predictions about co-regulated genes and regulatory networks in cells.
COGRIM: Bayesian variable selection model for regulatory network inference through the integration of gene expression data, ChIP binding data and sequence motif data.
PHYLOCLUS: Suite of perl programs for clustering co-regulated genes based on phylogenetically discovered transcription factor binding motifs.
MOTIF CLUSTERING: Perl programs and supplemental material for clustering transcription factor binding motif matrices based on a hierarchical Bayesian model.
2. Bayesian Nonparametrics
Extensions of Dirichlet processes for grouped and ordered data. Alternative prior processes for non-parametric clustering. Tree-based approaches for high-dimensional settings.
3. Economics and Marketing
Estimating income volatility while allowing for heterogeneity over time and between individuals in the population. Exploring the relationship between income volatility and risk aversion. Modeling career choice as a function of risk aversion. Models for missing data in marketing research.
|Older Media Attention|
2015/10/13: Articles about my collaboration with researchers investigating why elephants get less cancer than humans: Newsweek and IFLscience
2008/02/17: Media Attention for our Baseball Fielding Research: AP Boston Globe Popular Science Wired Science Citizen
2008/02/11: Media/Blog Discussion of our NY Times article: ESPN article Freakonomics Blog Statistics Blog