Shane T. Jensen

• Desen Lin, Shane T. Jensen, Susan Wachter (2023), The price effects of greening vacant lots: how neighborhood attributes matter, Real Estate Economics, 51 (), pp. 573-610.
• J. Cui, Shane T. Jensen, J. Macdonald (2022), The effects of vacant lot greening and the impact of land use and business presence on crime, Environment and Planning B: Urban Analytics and City Science, 49 (), pp. 1147-1158.
• J. Macdonald, Viet Nguyen, Shane T. Jensen, C.C. Branas (2022), Reducing crime by remediating vacant lots: the moderating effect of nearby land uses, Journal of Experimental Criminology, 18 (), pp. 639-644.
• T. Tran, W. Tanner Porter, D.J. Salkald, M.A. Prusinki, Shane T. Jensen, Dustin Brisson (2021), Estimating disease vector population size from Citizen Science data, Journal of the Royal Society Interface, 18 (20210610).
• Colman Humphrey, Shane T. Jensen, Dylan Small, Rachel Thurston (2020), Analysis of urban vibrancy and safety in Philadelphia, Environment and Planning B: Urban Analytics and City Science, 47 (), pp. 1573-1587.
• 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.
• 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.
• 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.
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Past Courses

• GCB9950 - Dissertation

  Ph.D. students enroll in this course after passing their candidacy exam. They work on their dissertation full-time under the guidance of their dissertation supervisor and other members of their dissertation committee.

• STAT1020 - Intro Business Stat

  Continuation of STAT 1010 or STAT 1018. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications. This course may be taken concurrently with the prerequisite with instructor permission.

• STAT1110 - Introductory Statistics

  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. Knowledge of high school algebra is required for this course.

• STAT3990 - Independent Study

  Written permission of instructor and the department course coordinator required to enroll in this course.

• STAT4420 - Intro Bayes Data Analys

  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. A course in probability (STAT 4300 or equivalent); a course in statistical inference (STAT 1020, STAT 1120, STAT 4310 or equivalent); and experience with the statistical software R (at the level of STAT 4050 or STAT 4700) are recommended.

• STAT5420 - Bayesian Meth & Comp

  Sophisticated tools for probability modeling and data analysis from the Bayesian perspective. Hierarchical models, mixture models and Monte Carlo simulation techniques.

• STAT8990 - Independent Study

  Written permission of instructor, the department MBA advisor and course coordinator required to enroll.

• STAT9270 - Bayesian Statistics

  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.

• STAT9950 - Dissertation

  Dissertation

• STAT9990 - Independent Study

  Written permission of instructor and the department course coordinator required to enroll.

• JASA Reproducibility Award, 2023 Description

  American Statistical Association

• Wharton Undergraduate Teaching Excellence Award, 2021
• Wharton Teaching Excellence Award, 2020
• Wharton Teaching Excellence Award, 2019
• SABR Analytics Conference Research Award in Contemporary Baseball Analysis, 2016 Description

  for the paper “OpenWAR: an open source system for evaluating overall player performance in major league baseball.”

• Sports in Statistics Award for Contributions to the Statistics in Sports Community, American Statistical Association, 2011
• David W. Hauck Award for Excellence in Undergraduate Teaching, The Wharton School, 2009
• Leonard J. Savage Award for best thesis in Application Methodology from the International Society for Bayesian Analysis, 2005

Knowledge @ Wharton

• How Dynamic Electricity Pricing Can Improve Market Efficiency, Knowledge @ Wharton - 11/12/2024
• Is Algorithmic Management Too Controlling?, Knowledge @ Wharton - 11/12/2024
• The Hidden Cost of AI Energy Consumption, Knowledge @ Wharton - 11/12/2024
• Charitable and Political Giving | Pinar Yildirim, Knowledge @ Wharton - 11/12/2024
• The Future of Banking, Knowledge @ Wharton - 11/11/2024
• Election Statistics, Knowledge @ Wharton - 11/06/2024
• Robots Are Taking Over Low-skilled Jobs — and Changing Votes, Knowledge @ Wharton - 11/05/2024
• Crypto Politics: Liberals’ and Conservatives’ Confidence in Cryptocurrencies, Knowledge @ Wharton - 11/05/2024
• Leadership and Elections | Mike Useem, Knowledge @ Wharton - 11/05/2024
• Automation Is Harming Low-skilled Workers and Possibly Their Voting Preferences, Knowledge @ Wharton - 11/01/2024