Bernard G. Greenberg Distinguished Lecture Series
2022 Greenberg Lecture Series – May 12 and 13, 2022
Andrew Gelman, PhD, Professor, Department of Statistics and Department of Political Science, Columbia University
Details for the three lectures (PDF)
Probability theory is false. Weak priors give strong and implausible posteriors. If you could give me your subjective prior I wouldn't need Bayesian inference. The best predictive model averaging is non-Bayesian. There will always be a need to improve our models. Nonetheless, we still find Bayesian inference to be useful. How can we make the best use of Bayesian methods in light of all their flaws?
The three central challenges of statistics are generalizing from sample to population, generalizing from control to treated group, and generalizing from observed data to underlying constructs of interest. These are associated with separate problems of sampling, causal inference, and measurement, but in real decision problems all three issues arise. We discuss the way in which varying treatment effects (interactions) bring sampling concerns into causal inference, along with the real challenges of applying this insight into real problems. We consider applications in medical studies, A/B testing, social science research, and policy analysis.
Statistical modeling has three steps: model building, inference, and model checking, followed by possible improvements to the model and new data that allow the cycle to continue. But we have recently become aware of many other steps of statistical workflow, including simulated-data experimentation, model exploration and understanding, and visualizing models in relation to each other. Tools such as data graphics, sensitivity analysis, and predictive model evaluation can be used within the context of a topology of models, so that data analysis is a process akin to scientific exploration. We discuss these ideas of dynamic workflow along with the seemingly opposed idea that statistics is the science of defaults. We need to expand our idea of what data analysis is, in order to make the best use of all the new techniques being developed in statistical modeling and computation.
Past Speakers
2021- Dr. Xihong Lin, Harvard University
Professor Xihong Lin
Dr. Xihong Lin, winner of the 2021 Greenberg Distinguished Lecturer Award, will present talks as part of the 2021 Bernard G. Greenberg Distinguished Lecture Series. Lin is a Professor and former Chair of the Department of Biostatistics, Coordinating Director of the Program in Quantitative Genomics at the Harvard T. H. Chan School of Public Health, and Professor of the Department of Statistics at the Faculty of Arts and Sciences of Harvard University, and Associate Member of the Broad Institute of Harvard and MIT.
2021 Greenberg Lecture flyer with links to register.
2019- Dr. Nicholas Jewell, University of California Berkeley
Dr. Nicholas Jewell
Dr. Nicholas Jewell, winner of the 2019 Greenberg Distinguished Lecturer Award, presented talks as part of the 2019 Bernard G. Greenberg Distinguished Lecture Series. Jewell is a Professor of Biostatistics and Statistics from the University of California Berkley. He received his PhD in mathematics from the University of Edinburgh in 1976.
2018- Dr. Jamie Robins, Harvard University
Dr. Jamie Robins
Dr. Jamie Robins, winner of the 2018 Greenberg Distinguished Lecturer Award, presented talks on May 14 and 15 as part of the 2018 Bernard G. Greenberg Distinguished Lecture Series. Robins is a Mitchell L. and Robin LaFoley Dong Professor of Epidemiology at Harvard University. He received his MD from the Washington University School of Medicine in 1976.
2017- Dr. Robert E. Kass, Carnegie Mellon
Dr. Robert E. Kass
Dr. Robert E. Kass, winner of the 2017 Greenberg Distinguished Lecturer Award, presented talks on May 15 and 16 as part of the 2017 Bernard G. Greenberg Distinguished Lecture Series. Kass is a Maurice Falk Professor of Statistics and Computational Neuroscience at Carnegie Mellon University. He received his doctorate in statistics from the University of Chicago and has been been on the faculty of the Department of Statistics at Carnegie Mellon since 1981.
2016 – Dr. James O. Berger, Duke University
Dr. James Berger
James O. Berger, PhD, winner of the 2016 Greenberg Distinguished Lecturer Award, presented three talks on May 12 and 13 as part of the 2016 Bernard G. Greenberg Distinguished Lecture Series. Berger’s lectures included “The Use of Rejection Odds and Rejection Ratios in Testing Hypotheses,” [PDF] “The Progress on the Foundations of Bayesian-Frequentist Unification” [PDF] and “Bayesian Multiplicity Control” [PDF].
2015 – Dr. Susan A. Murphy, University of Michigan
Susan A. Murphy
Photo Courtesy of the John D. and Catherine T. MacArthur Foundation
Dr. Susan A. Murphy, winner of the 2015 Greenberg Distinguished Lecturer Award, presented talks on May 11 and 12 as part of the 2015 Bernard G. Greenberg Distinguished Lecture Series. Dr. Murphy is a H.E. Robbins Distinguished University Professor of statistics and professor of psychiatry at the University of Michigan. She received her doctorate in statistics from UNC-Chapel Hill and was named a John D. and Catherine T. MacArthur Foundation Fellow for her work in designing the Sequential Multiple Assignment Randomized Trial, or SMART.
2014 – Dr. Jianqing Fan, Princeton University
Dr. Jianqing Fan
2013 – Dr. Trevor Hastie, Stanford University
Dr. Trevor Hastie
Dr. Trevor Hastie, winner of the 2013 Greenberg Distinguished Lecturer Award, presented talks on May 8 and 9 as part of the 2013 Bernard G. Greenberg Distinguished Lecture Series. Hastie is a professor of statistics and professor of health, research and policy at Stanford University. Hastie’s lectures included “Sparse Linear Models” [PDF] “Matrix Completion and Large Scale SVD Computation” [PDF] and “Graphical Model Selection” [PDF].
2012 – Dr. Robert John Tibshirani, Stanford University
Dr. Robert Tibshirani
2011 – Dr. Roderick Little, University of Michigan
Dr. Roderick J.A. Little, PhD
Dr. Roderick Little, winner of the 2011 Greenberg Distinguished Lecturer Award, presented talks on May 12 and 13 as part of the 2011 Bernard Greenberg Distinguished Lecture Series. Little is the Richard D. Remington Collegiate Professor of Biostatistics at University of Michigan. Little’s lectures included “Calibrated Bayes: Spanning the Divide Between Frequentist and Bayesian Inference” [PDF] “Some Methods for Handling Missing Values in Outcome Variables” [PDF] “Subsample Ignorable Likelihood Methods for Regression with Missing Values of Covariates – throwing data away can actually pay!” [PDF] and “Measurement Error as Missing Data: The Case of Epidemiologic Assays” [PDF].
2010 – Dr. Marvin Zelen, Harvard University
Dr. Marvin Zelen
Dr. Marvin Zelen, winner of the 2010 Greenberg Distinguished Lecturer Award, presented talks as part of the 2010 Bernard Greenberg Distinguished Lecture Series. Dr Marvin Zelen is a Lemuel Shattuck Research Professor of Statistical Science in the department of biostatistics at Harvard University. View the presentation slides.
2009 – Niels Keiding, University of Copenhagen
Niels Keiding
Niels Keiding, winner of the 2009 Greenberg Distinguished Lecturer Award, presented talks on May 4 and 5 as part of the 2009 Bernard Greenberg Distinguished Lecture Series. Keiding is the director of the Danish Graduate School in Biostatistics at the University of Copenhagen. Keiding’s lectures included “Event history analysis and the cross-section” [PDF] “Time-to-pregnancy: classical designs” [PDF]”Time to pregnancy: current duration data” [PDF] and “Describing episodes of drug treatment from joint observation of a prescription registry and a cross-sectional survey” [PDF].
