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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Mulholland, J. and Shane T. Jensen (Forthcoming), Predicting the future of free agent receivers and tight ends in the NFL.
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.
Ville Satopää, Shane T. Jensen, Robin Pemantle, Lyle H. Ungar (2017), Partial Information Framework: Model-Based Aggregation of Estimates from Diverse Information Sources, Electronic Journal of Statistics, 11, pp. 3781-3814.
Daniel Barth, Stephen H. Shore, Shane T. Jensen (2017), Identifying Idiosyncratic Career Taste and Skill with Income Risk, Quantitative Economics, 8, pp. 553-587.
J. Des Parkin, James D. San Antonio, Anton V. Persikov, Hayat Dagher, Raymond Dalgleish, Shane T. Jensen, Xavier Jeunemaitre, Judy Savige (2017), The collαgen III fibril has a “flexi-rod” structure of flexible sequences interspersed with rigid bioactive domains including two with hemostatic roles, PLoS One, 12(7): e0175582.
Lisa M. Abegglen, Aleah F. Caulin, Ashley Chan, Kristy Lee, Rosann Robinson, Michael S. Campbell, Wendy K. Kiso, Dennis L. Schmitt, Peter J. Waddell, Srividya Bhaskara, Shane T. Jensen, Carlo C. Maley, Joshua D. Schiffman (2016), Potential Mechanisms for Cancer Resistance in Elephants and Comparative Cellular Response to DNA Damage in Humans, Journal of the American Medical Association, 314, pp. 1850-1860.
Maryam Yousefi, Ning Li, Angela Nakauka-Ddamba, Shan Wang, Kimberly Davidow, Jenna Schoenberger, Zhengquan Yu, Shane T. Jensen, Michael G. Kharas, Christopher J. Lengner (2016), Msi RNA binding proteins control reserve intestinal stem cell quiescence, Journal of Cell Biology, 215, pp. 401-413.
Ning Li, Angela Nakauka-Ddamba, John Tobias, Shane T. Jensen, Christopher J. Lengner (2016), Mouse Label-Retaining Cells Are Molecularly and Functionally Distinct From Reserve Intestinal Stem Cells, Gastroenterology, 151, pp. 298-310.
Drausin Wulsin, Shane T. Jensen, Brian Litt (2016), Nonparametric Multi-level Clustering of Human Epilepsy Seizures, Annals of Applied Statistics, 10, pp. 667-689.
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.
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