Shane T. Jensen

Shane T. Jensen
  • Associate Professor of Statistics

Contact Information

  • office Address:

    463 Jon M. Huntsman Hall
    3730 Walnut Street
    Philadelphia, PA 19104

Research Interests: applications in bioinformatics, bayesian multi-level modeling, statistical computing and mcmc methods, statistics in sports

Links: CV

Overview

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)
Sports in Statistics Award for Contributions to the Statistics in Sports Community, American Statistical Association (2011)

Academic Positions Held

Wharton: 2004-present

For more information, go to My Personal Page

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Research

Teaching

Current 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.

    STAT102001

    STAT102002

    STAT102003

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.

  • STAT399 - INDEPENDENT STUDY

  • STAT442 - 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.

  • 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.

  • STAT927 - 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.

  • STAT995 - DISSERTATION

  • STAT999 - INDEPENDENT STUDY

Awards and Honors

  • 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

Activity

Latest Research

Mulholland, J. and Shane T. Jensen (Forthcoming), Predicting the future of free agent receivers and tight ends in the NFL.
All Research

In the News

How Urban Planners Can Encourage ‘Vibrancy’ — and Create Safer Cities

Recent Wharton research examines how healthy energy in a particular neighborhood can help reduce crime.

Knowledge @ Wharton - 2017/06/12
All News

Awards and Honors

SABR Analytics Conference Research Award in Contemporary Baseball Analysis 2016
All Awards