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
Elected Fellow of the International Society for Bayesian Analysis (2014); Elected Fellow of the American Statistical Association (1997); Elected 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
Editor, Annals of Statistics, 2016-2018; Executive Editor, Statistical Science, 2004-2007.
For more information, go to My Personal Page
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).
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.
Arun Kumar Kuchibhotla, Lawrence D. Brown, Andreas Buja, Edward I. George, Linda Zhao (2020), Valid Post-selection Inference in Assumption-lean Linear Regression, Annals of Statistics, (to appear).
Abstract: This paper provides multiple approaches to perform valid post-selection inference in an assumption-lean regression analysis. To the best of our knowledge, this is the first work that provides valid post-selection inference for regression analysis in such a general settings that include independent, m-dependent random variables.
Yuehan Yang, Ji Zhu, Edward I. George (Under Review), MSP: A Multi-step Screening Procedure for Sparse Recovery.
Gemma Moran, Veronika Rockova, Edward I. George (2019), Variance Prior Forms for High-Dimensional Bayesian Variable Selection, Bayesian Analysis, 14 (4), pp. 1091-1119.
Andreas Buja, Lawrence D. Brown, Richard A. Berk, Edward I. George, Emil Pitkin, Mikhail Traskin, Kai Zhang, Linda Zhao (2019), Models as Approximations I: Consequences Illustrated with Linear Regression, Statistical Science, 34 (4), pp. 523-544.
Andreas Buja, Lawrence D. Brown, Arun Kumar Kuchibhotla, Richard A. Berk, Edward I. George, Linda Zhao (2019), Models as Approximations II: A Model-Free Theory of Parametric Regression, Statistical Science, 34 (4), pp. 345-365.
Cecilia Balocchi, Sameer K. Deshpande, Edward I. George, Shane T. Jensen (Under Review), Crime in Philadelphia: Bayesian Clustering with Particle Optimization.
This This class will cover the fundamental concepts of statistical inference. Topics include sufficiency, consistency, finding and evaluating point estimators, finding and evaluating interval estimators, hypothesis testing, and asymptotic evaluations for point and interval estimation.
Written permission of instructor and the department course coordinator required to enroll in this course.
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 621 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 613, 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, the department MBA advisor and course coordinator required to enroll.
Written permission of instructor and the department course coordinator required to enroll.