Edward I. George

• Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja, Edward I. George, Linda Zhao (2021), Uniform-in-Submodel Bounds for Linear Regression in a Model Free Framework, Econometric Theory, (in press) ().
• Hugh A. Chipman, Edward I. George, Robert E. McCulloch, Thomas S. Shively (2021), mBART: Multidimensional Monotone BART, Bayesian Analysis, (in press) ().
• Edward I. George, Gourab Mukherjee, Keisuke Yano (2021), Optimal Shrinkage Estimation of Predictive Densities Under α-Divergences, Bayesian Analysis, (in press) ().
• Morris L. Eaton and Edward I. George (2021), Charles Stein and Invariance: Beginning with the Hunt-Stein Theorem by Morris, Annals of Statistics, (to appear) ().
• Marilena Barbieri, James O. Berger, Edward I. George, Veronika Rockova (2021), The Median Probability Model and Correlated Variables, Bayesian Analysis, (in press) ().
• Yuehan Yang, Ji Zhu, Edward I. George (2021), MuSP: A Multistep Screening Procedure for Sparse Recovery, Stat, 10(1):e352 ().
• Cecilia Balocchi, Ray Bai, Jessica Liu, Silvia P. Canelon, Edward I. George, Yong Chen, Mary R. Boland (Under Review), A Bayesian Hierarchical Modeling Framework for Geospatial Analysis of Adverse Pregnancy Outcomes.
• Gemma E. Moran, Veronika Rockova, Edward I. George (2021), Spike-and-Slab Lasso Biclustering, Annals of Applied Statistics, 15 (1), pp. 148-173.
• Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja, Edward I. George, Linda Zhao (2020), A Model Free Perspective for Linear Regression: Uniform-in-model Bounds for Post Selection Inference, Econometric Theory, (to appear) (). Abstract

  For the last two decades, high-dimensional data and methods have proliferated
  throughout the literature. The classical technique of linear regression, however, has not lost
  its touch in applications. Most high-dimensional estimation techniques can be seen as variable
  selection tools which lead to a smaller set of variables where classical linear regression
  technique applies. In this paper, we prove estimation error and linear representation bounds
  for the linear regression estimator uniformly over (many) subsets of variables. Based on deterministic
  inequalities, our results provide “good” rates when applied to both independent
  and dependent data. These results are useful in correctly interpreting the linear regression
  estimator obtained after exploring the data and also in post model-selection inference. All
  the results are derived under no model assumptions and are non-asymptotic in nature.

• Richard A. Berk, Andreas Buja, Lawrence D. Brown, Edward I. George, Arun Kumar Kuchibhotla, Weijie Su, Linda Zhao (2020), Assumption Lean Regression, American Statistician, (in press) ().
• All Research from Edward I. George »

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.

• 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.

• STAT6210 - Acc Regression Analysis

  STAT 6210 is intended for students with recent, practical knowledge of the use of regression analysis in the context of business applications. This course covers the material of STAT 6130, but omits the foundations to focus on regression modeling. The course reviews statistical hypothesis testing and confidence intervals for the sake of standardizing terminology and introducing software, and then moves into regression modeling. The pace presumes recent exposure to both the theory and practice of regression and will not be accommodating to students who have not seen or used these methods previously. The interpretation of regression models within the context of applications will be stressed, presuming knowledge of the underlying assumptions and derivations. The scope of regression modeling that is covered includes multiple regression analysis with categorical effects, regression diagnostic procedures, interactions, and time series structure. The presentation of the course relies on computer software that will be introduced in the initial lectures. Recent exposure to the theory and practice of regression modeling is recommended.

• STAT9950 - Dissertation

  Dissertation

• STAT9990 - Independent Study

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

• Cornell University Distinguished Alumni for the Department of Statistical Sciences, 2018
• Simons Fellowship, Isaac Newton Institute for Mathematical Sciences, Cambridge, 2018
• Bohrer Lecturer, University of Illinois at Urbana-Champaign, 2017
• Fellow, International Society for Bayesian Analysis, 2014
• Challis Lecturer and Award for Outstanding Contributions to Statistics, University of Florida, 2012
• Geisser Distinguished Lecturer, University of Minnesota, 2012
• Palmetto Lecturer, University of South Carolina, 2012
• Loeb Lecturer, Washington University in St. Louis, 2011
• Medallion Lecturer, Institute of Mathematical Statistics, 2010
• Penn IUR Faculty Fellow, 2009
• Hartley Memorial Lecturer, Texas A&M University, 2007
• Wharton Core Professor Award, "Goes Above and Beyond", 2004
• ISI Highly Cited Researcher in Mathematics, 2004
• Wharton Core Professor Award, "Tough But We'll Thank in Five Years", 2004
• Excellence in Education Award, The University of Texas at Austin, 2001
• Faculty Honor Roll for Core Class Teaching, The University of Texas at Austin, 2000
• Lawrence Baxter Memorial Lecturer, SUNY at Stony Brook, 1998
• Dean's Fellow, The University of Texas at Austin, 1998
• CBA Foundation Award for Outstanding Research Contributions, The University of Texas at Austin, 1998
• Fellow, American Statistical Association, 1997
• Fellow, Center for Management of Operations and Logistics, The University of Texas at Austin, 1996
• Member, International Statistical Institute, 1996
• Joe D. Beasley Award for Teaching Excellence, The University of Texas at Austin, 1996
• Fellow, Institute of Mathematical Statistics, 1995
• CBA Foundation Award for Research Excellence, The University of Texas at Austin, 1995
• GBC Award for Excellence in Teaching of the Core Curriculum, Graduate School of Business, The University of Texas at Austin, 1993
• Spurgeon Bell Centennial Fellowship, Graduate School of Business, The University of Texas at Austin, 1993
• The Emory Williams Award for Excellence in Teaching, The University of Chicago, 1987
• McKinsey Award for Excellence in Teaching, The University of Chicago, 1987
• DeGroot Lecture, Department of Statistics, Carnegie Mellon University, 1970
• Yakovlev Colloquium, Department of Biostatistics and Computational Biology, University of Rochester, 1970

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