Abraham J. Wyner

• Ryan Brill, Abraham J. Wyner, Ian Barnett (Working), Entropy-Based Strategies for Multi-Bracket Pools.
• Ryan Brill, Sameer Deshpande, Abraham J. Wyner (2023), A Bayesian Analysis of the Time Through the Order Penalty in Baseball, Journal of Quantitative Analysis in Sports, 19 (4), pp. 245-262.
• Matthew A. Olson and Abraham J. Wyner (Working), Making Sense of Random Forest Probabilities: a Kernel Perspective.
• Matthew Olson, Abraham J. Wyner, Richard A. Berk (2018), Modern Neural Networks Generalize Well on Small Data Sets, Advances in Neural Information Processing Systems (NIPS).
• Matthew Olson and Abraham J. Wyner (Under Review), Do Random Forests Estimate Class Probabilities?.
• Matthew Olson, Abraham J. Wyner, Richard A. Berk (Under Review), Generalizations of the Random Forest Kernel.
• Sameer Deshpande and Abraham J. Wyner (2017), A Hierarchical Bayesian Model of Pitch Framing, Journal of Quantitative Analysis in Sports, 13 (3), pp. 95-112.
• Philip A. Ernst, Larry Shepp, Abraham J. Wyner (2017), Yule’s “Nonsense Correlation” Solved!, The Annals of Statistics, 45 (4), pp. 1789-1809.
• Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease (2017), Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers, Journal of Machine Learning Research, 18 (), pp. 1-33.
• Blakely McShane, Shane T. Jensen, Allan Pack, Abraham J. Wyner (2013), Statistical Learning With Time Series Dependence: An Application to Scoring Sleep in Mice. Discussion paper with rejoinder, Journal of American Statistical Association, 108 (), pp. 1147-1172. Abstract

  We develop methodology that combines statistical learning methods with generalized Markov models, thereby enhancing the former to
  account for time series dependence. Our methodology can accommodate very general and very long-term time dependence structures in an easily estimable and computationally tractable fashion. We apply our methodology to the scoring of sleep behavior in mice. As methods currently used to score sleep in mice are expensive, invasive, and labor intensive, there is considerable interest in developing high-throughput automated systems which would allow many mice to be scored cheaply and quickly. Previous efforts at automation have been able to differentiate sleep from wakefulness, but they are unable to differentiate the rare and important state of rapid eye movement (REM) sleep from non-REM sleep. Key difficulties in detecting REM are that (i) REM is much rarer than non-REM and wakefulness, (ii) REM looks similar to non-REM in terms of the observed covariates, (iii) the data are noisy, and (iv) the data contain strong time dependence structures crucial for differentiating REM from non-REM. Our new approach (i) shows improved differentiation of REM from non-REM sleep and (ii) accurately estimates aggregate quantities of sleep in our application to video-based sleep scoring of mice. Supplementary materials for this article are available online.

• All Research from Abraham J. Wyner »

Past Courses

• AMCS5999 - Independent Study

  Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.

• AMCS9999 - Ind Study & Research

  Study under the direction of a faculty member.

• STAT1010 - Intro Business Stat

  Data summaries and descriptive statistics; introduction to a statistical computer package; Probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariate data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. This course will have a business focus, but is not inappropriate for students in the college. This course may be taken concurrently with the prerequisite with instructor permission.

• STAT3990 - Independent Study

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

• STAT4010 - Sports Analytics

  This course would introduce undergraduate students to the growing field of sports analytics, while allowing them to implement and integrate their knowledge base by exploring real sports data sets to solve real problems. While the context will be sports related, the skills and techniques gained will be widely applicable and generalizable with applications in diverse areas. Prerequisites: Must be a declared Statistics Concentrator or Business Analytics Concentrator or Statistics Minor or Data Science Minor. Permission from the Instructor is required. An interest in sports is highly recommended.

• STAT6130 - 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.

• STAT7250 - Sports and Gaming Analytics

  The “Moneyball revolution” in sports has inspired great interest in the transformative potential of statistics. This “1/2 credit course will introduce students to the growing field of sports analytics while creating for students an opportunity to practice and improve their analytical skills on real problems that are accessible and fun for anyone. This course is meant for students with an interest in sports and a foundational knowledge of statistics. While the context will be sports related and the expectation of students is that they are interested and knowledgeable about most major sports, the skills and techniques gained will be widely applicable and generalizable with applications in diverse areas. The course is very applied and very data driven. Students will conduct hands on work with real data using JMP software, R or Python. Along the way, students will learn new techniques for analyzing data and gain practical and useful skills that will be broadly applicable across many areas.

• STAT8990 - Independent Study

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

• STAT9950 - Dissertation

  Dissertation

• Mathematical Sciences Post-Doctoral Fellow, NSF, 1995

• Penn Professors Findings Contradict Clemens' Analysis of Career Stats, ESPN - 02/10/2008

Knowledge @ Wharton

• The EV Trade War Alone Won’t Save American Manufacturing, Knowledge @ Wharton - 02/11/2025
• AI’s Impact on Productivity and Innovation | Daniel Rock, Knowledge @ Wharton - 02/11/2025
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• Why Accountability Matters in AI Development and Governance, Knowledge @ Wharton - 02/07/2025
• Embracing AI: Are You a Doomer, Gloomer, Zoomer, or Bloomer?, Knowledge @ Wharton - 02/04/2025
• Can Pride Make You Look Incompetent at Work?, Knowledge @ Wharton - 02/04/2025
• Transformative Tech: Innovating for the Underserved | Valentina Assenova, Knowledge @ Wharton - 02/04/2025
• As People Still Recover From the LA Wildfires, What Can Business Leaders Do to Help?, Knowledge @ Wharton - 02/04/2025
• The Economic Impact of Trump’s Tariffs, Knowledge @ Wharton - 02/03/2025
• Building a Brand: The Entrepreneurial Lessons Learned on the Road to Cirkul’s Success, Knowledge @ Wharton - 01/30/2025