“How Auxiliary Information Can Help Your Missing Data Problem,” presented by Dr. Jerry Reiter, Duke University
Events
Makuch Distinguished Lecture featuring Susan A. Murphy, 11/10 @ 4pm
Robert W. Makuch Distinguished Lecture in Biostatistics Featuring Susan A. Murphy Mallinckrodt Professor of Statistics and Computer Science Radcliffe Alumnae Professor at the Radcliffe Institute Harvard University Inference Using Adaptively Collected Data Bandit algorithms are increasingly used in real-world sequential decision-making problems. Associated with this is an increased desire to be able to use the resulting datasets […]
Paper of the Month: November 2021
Bradley Efron (2020) Prediction, Estimation, and Attribution.pdf, Journal of the American Statistical Association, 115:530, 636-655, DOI: 10.1080/01621459.2020.1762613.
Paper of the Month: October 2021
Leon Bottou. Online learning and stochastic approximations. In D Saad, editor, Online Algorithms and Stochastic Approximations. Cambridge University Press, Cambridge, U.K., 1998.
Interdisciplinary Seminar: Fan Li, Duke University
“Overlap Weighting for Causal Inference,” presented by Dr. Fan Li, Duke University
Interdisciplinary Seminar: Susan Murphy, Harvard University
“Assessing Personalization in Digital Health,” presented by Dr. Susan Murphy, Harvard University
An Introduction to the UConn Statistics Cluster (September 13, 2021)
All faculty and graduate students in the Department of Statistics are invited. Time: September 13, 3:35 pm to 4:25 pm Place: AUST 103 Speaker: Daniel Prather, Administrator for the Statistics Computer Cluster Title: An Introduction to the UConn Statistics Cluster
Paper of the Month: September 2021
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581-592., Cambridge, U.K., 1998.
Interdisciplinary Seminar: Jon Krosnick, Stanford University
“The Collapse of Scientific Standards in the World of High Visibility Survey Research,” presented by Dr. Jon Krosnick, Stanford University
Interdisciplinary Seminar: David Kaplan, University of Wisconsin-Madison
“Developments and Extensions in the Quantification of Model Uncertainty: A Bayesian Perspective,” presented by David Kaplan, University of Wisconsin-Madison