A 2018 Cornell Distinguished Alumni recipient, Edward George is currently a professor of statistics at the Wharton School of the University of Pennsylvania. His research interests include hierarchical modeling, model uncertainty, shrinkage estimation, treed modeling, variable selection, and wavelet regression.Having received his bachelor's degree in mathematics from Cornell, George went on to earn a master's from SUNY at Stony Brook and a PhD from Stanford University. He has held previous appointments at the University of Texas at Austin and the University of Chicago.
Among his many accomplishments, Dr. George is a fellow of the International Society for Bayesian Analysis (2014), the American Statistical Association (1997), and the Institute of Mathematical Statistics (1995). He also served as editor of the Annals of Statistics from 2016 to 2018, and executive editor of Statistical Science from 2004 to 2007.
Talk: Multidimensional Monotonicity Discovery with MBART
Abstract: For the discovery of a regression relationship between y and x, a vector of p potential predictors, the flexible nonparametric nature of BART (Bayesian Additive Regression Trees) allows for a much richer set of possibilities than restrictive parametric approaches. To exploit the potential monotonicity of the predictors, we introduce mBART, a constrained version of BART that incorporates monotonicity with a multivariate basis of monotone trees, thereby avoiding the further confines of a full parametric form. Using mBART to estimate such effects yields (i) function estimates that are smoother and more interpretable, (ii) better out-of-sample predictive performance and (iii) less post-data uncertainty. By using mBART to simultaneously estimate both the increasing and the decreasing regions of a predictor, mBART opens up a new approach to the discovery and estimation of the decomposition of a function into its monotone components. (This is joint work with H. Chipman, R. McCulloch and T. Shively).