Andrew Gelman, Ph.D., is Higgins Professor of Statistics, Professor of Political Science, and director of the Applied Statistics Center at Columbia University.
Gelman has received the Outstanding Statistical Application award (three times) from the American Statistical Association, the award for the 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 for outstanding contributions by a person under the age of forty.
His books include:
Bayesian Data Analysis
Teaching Statistics: A Bag of Tricks
Data Analysis Using Regression and Multilevel/Hierarchical Models
Red State, Blue State, Rich State, Poor State: Why Americans Vote the
Way They Do
A Quantitative Tour of the Social Sciences
Regression and Other Stories
Gelman’s research spans a wide range of topics, including why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City; the statistical challenges of estimating small effects; the probability that one vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in home basements; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.
You can visit his university webpage here.
You can visit his personal webpage here.
According to Google Scholar, his work has been cited more than 160,000 times.
Multilevel Modeling for Design and Analysis
Wednesday, May 31, 2023
Traditional statistical methods need to be updated when we move beyond simple models of random sampling, constant effects, and accurate measurements. In this seminar, we consider general challenges of design and analysis in a world of nonrandom samples, varying treatment...View Details