Prediction, Estimation, and Attribution

Thursday, October 1, 2020, 2:30-3:50pm, virtual access only

Bradley Efron, PhD
Max H. Stein Professor and Professor of Statistics and of Biomedical Data Science
Stanford University

Title: Prediction, Estimation, and Attribution

Abstract: The scientific needs and computational limitations of the Twentieth Century fashioned classical statistical methodology. Both the needs and limitations have changed in the Twenty-First, and so has the meth-odology. Large-scale prediction algorithms – neural nets, deep learning, boosting, support vector machines, random forests – have achieved star status in the popular press. They are recognizable as heirs to the regression tradition, but ones carried out at enormous scale and on titanic data sets. How do these algorithms compare with standard regression techniques such as Ordinary Least Squares or logistic regression? Several key discrepancies will be examined, centering on the differences between prediction and estimation or prediction and attribution (that is, significance testing). Most of the discussion is carried out through small numerical examples. The talk does not assume familiarity with prediction algorithms.