Statistics and Data Science: the Prediction and Modeling Cultures

Seminar | March 5 | 4-5 p.m. | 102 Moffitt Undergraduate Library

 Roderick Little, University of Michigan

 Department of Statistics

I recently taught a course entitled "Seminal Papers and Controversies in Statistics", and Leo Breiman's (2001) article "Statistical Modeling: The Two Cultures" was a very popular paper with students. The paper contrasts the machine learning culture, with it's focus on prediction, with more classical parametric modeling approach to statistics. I am more in the parametric modeling camp, but appreciate the prediction perspective as yielding a simple and unified approach to problems in statistics – the overarching objective being to predict the things you don’t know, with appropriate measures of uncertainty. Philosophically I try to follow the "calibrated Bayes" perspective of Box and Rubin. I discuss this viewpoint, tying it to other seminal papers in my course and two recent applications to missing data and causal inference.