Date/Time
Date(s) - 28/04/2025
9:00 am - 11:00 am
Category(ies) No Categories
Joint Program in Survey Methodology
JPSM Distinguished Lecture 2025: Andrew Gelman, Ph.D.
Generalizing for Sampling and Causal Inference
Monday, April 28, 2025
3:00 PM – 5:00PM
1101 A. James Clark Hall
University of Maryland, College Park
Register here: https://jpsm.umd.edu/form/jpsm-distinguished-lecture-2025
Sponsored by: Joint Program in Survey Methodology, University of Maryland, College Park, MD.
Co-Sponsor by: International Association of Survey Statisticians (IASS), and Washington Statistical Society (WSS).
Abstract: We can combine model and design-based inference to address the following challenges of generalizing from sample to population: sparse data, small-area estimation, adjustment for non-census variables, cluster sampling, and survey weights. The methods are intellectually exciting and also important in the real world, as we demonstrate using examples in public health and public opinion, medical research, and policy analysis.
Bio: Dr. Andrew Gelman is a professor of statistics and political science at Columbia University. He received his Ph.D. and M.A. in Statistics from Harvard University. He has received the Outstanding Statistical Application Award three times from the American Statistical Association, the award for Best Article published in the American Political Science Review, the Mitchell and DeGroot prizes from the International Society of Bayesian Analysis, and the Council of Presidents of Statistical Societies Award. He is the author or co-author of several books, including Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin), Teaching Statistics: A Bag of Tricks (with Deborah Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina), and Regression and Other Stories (with Jennifer Hill and Aki Vehtari), Active Statistics (with Aki Vehtari) and the forthcoming Bayesian Workflow (with Aki Vehtari, Richard McElreath, and others). Dr. Gelman has conducted research on a wide range of topics, including why it is rational to vote; why campaign polls are so variable when elections are so predictable; the effects of incumbency and redistricting; reversals of death sentences; police stops in New York City; the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in basements; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.