Shane Jensen

Education

PhD, Harvard University, 2004
AM, Harvard University, 2001
MS, McGill University, 1999
BS, McGill University, 1997

Career and Recent Professional Awards

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)

Academic Positions Held

Wharton: 2004present

For more information, go to My Personal Page

Stephen H. Shore, Daniel Barth, Shane T. Jensen (2016), Identifying Idiosyncratic Career Taste and Skill with Income Risk, Quantitative Economics, to appear.

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. 18501860.

Ning Li, Angela NakaukaDdamba, John Tobias, Shane T. Jensen, Christopher J. Lengner (2016), Mouse LabelRetaining Cells Are Molecularly and Functionally Distinct From Reserve Intestinal Stem Cells, Gastroenterology, 151, pp. 298310.

Drausin Wulsin, Shane T. Jensen, Brian Litt (2016), Nonparametric Multilevel Clustering of Human Epilepsy Seizures, Annals of Applied Statistics, 10, pp. 667689.

Ning Li, Maryam Yousefi, Angela NakaukaDdamba, John Tobias, Shane T. Jensen, Edward E. Morrisey, Christopher J. Lengner (2016), Heterogeneity in Readouts of Canonical Wnt Pathway Activity Within Intestinal Crypts, Developmental Dynamics, 245, pp. 822833.

Sameer Deshpande and Shane T. Jensen (2016), Estimating an NBA player’s impact on his team’s chances of winning, Journal of Quantitative Analysis in Sports, 12. 10.1515/jqas20150027

Abstract: Traditional NBA player evaluation metrics are based on scoring differential or some paceadjusted linear combination of box score statistics like points, rebounds, assists, etc. These measures treat performances with the outcome of the game still in question (e.g. tie score with five minutes left) in exactly the same way as they treat performances with the outcome virtually decided (e.g. when one team leads by 30 points with one minute left). Because they ignore the context in which players perform, these measures can result in misleading estimates of how players help their teams win. We instead use a win probability framework for evaluating the impact NBA players have on their teams’ chances of winning. We propose a Bayesian linear regression model to estimate an individual player’s impact, after controlling for the other players on the court. We introduce several posterior summaries to derive rankorderings of players within their team and across the league. This allows us to identify highly paid players with low impact relative to their teammates, as well as players whose high impact is not captured by existing metrics.

Daniel McCarthy and Shane T. Jensen (2016), Power Weighted Densities for Time Series Data, Annals of Applied Statistics, 10, pp. 305334.

Aline Normoyle and Shane T. Jensen (2015), Bayesian Clustering of Player Styles for Multiplayer Games , Proceedings, The Eleventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE15).

Shane T. Jensen and Jordan Rodu (Working), Locating Recombination Hot Spots in Genomic Sequences through the Singular Value Decomposition.

Shane T. Jensen and Stephen H. Shore (2015), Changes in the Distribution of Earnings Volatility , Journal of Human Resources, 50.3, pp. 811836.

Past Courses

STAT102 INTRO BUSINESS STAT

Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications.

STAT111 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.

STAT542 BAYESIAN METH & COMP

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

STAT613 REGR ANALYSIS FOR BUS

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.

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 Urban Planners Can Encourage ‘Vibrancy’ — and Create Safer Cities, Knowledge @ Wharton 06/12/2017 Sports by the Numbers: Predicting Winners and Losers, Knowledge @ Wharton 04/20/2012 The Use — and Misuse — of Statistics: How and Why Numbers Are So Easily Manipulated, Knowledge @ Wharton 04/02/2008