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) ().
• Richard A. Berk, Andreas Buja, Lawrence D. Brown, Edward I. George, Arun Kumar Kuchibhotla, Weijie Su, Linda Zhao (2021), Assumption Lean Regression, American Statistician, 75 (1), pp. 76-84.
• 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.
• M.T. Pratola, H. A. Chipman, Edward I. George, R. E. McCulloch (2020), Heteroscedastic BART via Multiplicative Regression Trees, Journal of Computational and Graphical Statistics, (to appear) ().
• 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.

• 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

• 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

• The Fed Chair Transition and the Future of Central Bank Independence, Knowledge @ Wharton - 01/09/2026
• Special Report: The AI Skills Gap, Knowledge @ Wharton - 01/09/2026
• Six AI Trends Shaping Business, Education, and Markets in 2026, Knowledge @ Wharton - 01/07/2026
• Data-Driven NFL Playoffs and College Football’s Shifting Power, Knowledge @ Wharton - 01/07/2026
• The Psychology of New Year’s Resolutions | Katy Milkman, Knowledge @ Wharton - 01/06/2026
• How AI and Bayesian Models Are Reshaping Sports Analytics, Knowledge @ Wharton - 12/31/2025
• What to Expect for Real Estate in 2026, Knowledge @ Wharton - 12/31/2025
• What’s Ahead for the U.S. Economy?, Knowledge @ Wharton - 12/26/2025
• What Advanced Metrics Reveal About the 2025 NFL Season, Knowledge @ Wharton - 12/24/2025
• How the Job Market Is Shaping Up for 2026, Knowledge @ Wharton - 12/24/2025