Event Date
Speaker: Andrew Gelman, Professor, Department of Statistics and Department of Political Science, Columbia University.
Title: "What are the most important statistical ideas of the past 50 years?"
Abstract: We argue that the most important statistical ideas of the past half century are: counterfactual causal inference,
bootstrapping and simulation-based inference, overparameterized models and regularization, multilevel models, generic computation algorithms, adaptive decision analysis, robust inference, and exploratory data analysis. We discuss common features of these ideas, how they relate to modern computing and big data, and how they might be developed and extended in future decades. The goal of this talk is to provoke thought and discussion regarding the larger themes of research in statistics and data science. (This work is joint with Aki Vehtari and the paper is here: http://www.stat.columbia.edu/~gelman/research/unpublished/stat50.pdf).
About the speaker: Andrew Gelman is a professor of statistics and political science at Columbia University, and a prominent Bayesian Statistician. He is one of the authors of the seminal book: Bayesian Data Analysis (http://www.stat.columbia.edu/~gelman/book/) and the author of many important contributions in the area of Bayesian statistics (http://www.stat.columbia.edu/~gelman/research/published/). His blog can be found here: https://statmodeling.stat.columbia.edu. He has received the Outstanding Statistical Application award from the American Statistical Association three times. He is an elected fellow of the American Statistical Association and the Institute of Mathematical Statistics. He was elected fellow of the American Academy of Arts and Sciences (AAAS) in 2020.
Seminar Date/Time: Thursday April 1, 4:10pm
This seminar will be delivered remotely via Zoom. To access the Zoom meeting for this seminar, please contact the instructor Professor Jairo Fùquene Patiño or Pete Scully (pscully@ucdavis.edu) for the meeting ID and password, stating your affiliation.