409 Academic Research Building
265 South 37th Street
Philadelphia, PA 19104
Research Interests: hierarchical modeling, model uncertainty, shrinkage estimation, treed modeling, variable selection, wavelet regression
PhD, Stanford University, 1981
MS, SUNY at Stony Brook, 1976
AB, Cornell University, 1972
Fellow of the International Society for Bayesian Analysis (2014).
Fellow of the American Statistical Association (1997).
Fellow of the Institute of Mathematical Statistics (1995).
CBA Foundation Award for Outstanding Research Contributions (1998) and the CBA Foundation Award for Research Excellence (1995), The University of Texas at Austin.
Excellence in Education Award (2001) and the Joe D. Beasley Award for Teaching Excellence (1996), The University of Texas at Austin
McKinsey Award for Excellence in Teaching (1987) and the Emory Williams Award for Excellence in Teaching (1987), The University of Chicago.
Wharton: 2001-present (Chairperson, Statistics Department, 2008-2014; named Universal Furniture Professor, 2002)
Previous appointment: University of Texas at Austin, University of Chicago.
Visiting Appointments: Cambridge University; University of Paris; University of Valencia
Co-Editor, Annals of Statistics, 2016-2018; Executive Editor, Statistical Science, 2004-2007; President, International Society for Bayesian Analysis, 2003.
Marilena Barbieri, James O. Berger, Edward I. George, Veronika Rockova (2021), The Median Probability Model and Correlated Variables, Bayesian Analysis, (in press).
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).
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.
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).
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).
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.
Study under the direction of a faculty member.
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.
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.
Written permission of instructor and the department course coordinator required to enroll.
It’s a Wonderful Life is a Christmas classic, but Wharton statistics professor Edward George says it should also be required viewing for business leaders. …Read MoreKnowledge at Wharton - 4/8/2013