Seminar by Lan Wen
Student seminar series Lan Wen Link to join seminar: Hosted on Microsoft Teams |
Multiply robust estimators for causal effects of grace period treatment initiation strategies
Student seminar series Lan Wen Link to join seminar: Hosted on Microsoft Teams |
Multiply robust estimators for causal effects of grace period treatment initiation strategies
Probability seminar series Roland Bauerschmidt Link to join seminar: Hosted on Zoom |
Log-Sobolev inequality for near critical Ising and continuum
Statistics and Biostatistics seminar series Lucas Mentch Link to join seminar: Hosted on Zoom |
Random Forests: Why They Work and Why That's a Problem
Actuarial Science and Financial Mathematics seminar series Damir Filipović Link to join seminar: Hosted on Zoom |
Stripping the Discount Curve - a Robust Machine Learning Approach
Student seminar series Alexander Sharp Link to join seminar: Hosted on Microsoft Teams |
Characterizing the Asymptotic Properties of SEMmax Parameter Estimation
Statistics and Biostatistics seminar series Eunhye Song Link to join seminar: Hosted on Zoom |
Selection of the most probable best
Actuarial Science and Financial Mathematics seminar series Jennifer Alonso Garcia Link to join seminar: Hosted on Zoom |
A hybrid variable annuity contract embedded with living and death benefit riders
Probability seminar series Gautam Kamath |
Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism
Statistics and Biostatistics seminar series Issa Dahabreh Link to join seminar: Hosted on Zoom |
Causally interpretable meta-analysis: transporting inferences from multiple randomized trials to a target population
The Department of Statistics and Actuarial Science is very pleased to announce it is hosting a virtual conference on Biostatistics: Foundations and the Era of Data Science from April 28 - April 29, 2022. This conference will feature talks by leading statistical scientists addressing current challenges in health research. Many of the analytical and inferential issues arise from the need to fit models for complex processes using large administrative data sources. Time will be devoted to discussing challenges, opportunities, and areas warranting further methodological development.