Online Program Home
My Program
All Times EDT
Legend:
* = applied session ! = JSM meeting theme
Activity Details
24 * !
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Mon, 8/3/2020,
10:00 AM -
11:50 AM
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Virtual
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Causal Inference When the Outcome Is Truncated by Death — Topic Contributed Papers
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Section on Statistics in Marketing, Lifetime Data Science Section, Biometrics Section, Section on Statistics in Epidemiology
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Organizer(s): Jessica Gerald Young, Harvard Medical School
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Chair(s): Jessica Gerald Young, Harvard Medical School
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10:05 AM
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New Estimands for Causal Inference Conditional on Post-Treatment Variables
Mats Stensrud, Harvard T. H. Chan School of Public Health
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10:25 AM
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Principal Surrogate Evaluation Using Multiple Trials
Zhichao Jiang, University of Massachusetts, Amherst; Peng Ding, University of California, Berkeley; Zhi Geng, Peking University
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10:45 AM
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Flexible Estimation of Time-Varying Survivor Average Causal Estimands Using Bayesian Additive Regression Trees
Leah Comment, Foundation Medicine, Inc
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11:05 AM
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Bayesian Mediation Analysis for Cluster Randomized Trials
Joseph Hogan, Brown University; Michael Daniels, University of Florida
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11:25 AM
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Floor Discussion
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182
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Tue, 8/4/2020,
10:00 AM -
2:00 PM
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Virtual
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Modern Applications of Statistical Methods in Marketing — Contributed Papers
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Section on Statistics in Marketing
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Chair(s): Lan Luo, USC Marshall School of Business
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Immunogenicity Assessment and Extrapolation in BDRS
Xin Cao, Merck & Co Inc
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A New Procedure to Evaluate the Quality of Check All That Apply (CATA) Data
Presentation
Thierry Fahmy, Addinsoft
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A "Novel" Application of Representation Learning to Understand How People Consume Books
Sarah Cox, Penguin Random House
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Parallel Experimentation in a Competitive Advertising Marketplace
Caio Waisman, Northwestern University
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Using Bayesian Topic Modeling to Enhance Customer Purchase Prediction
Samuel Levy, Carnegie Mellon University; Dokyun Lee, Carnegie Mellon University; Daniel McCarthy, Emory University; Alan Montgomery, Carnegie Mellon University
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284 * !
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Wed, 8/5/2020,
10:00 AM -
11:50 AM
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Virtual
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Statistical Learning for Dependent and Complex Data: New Directions and Innovation — Invited Papers
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Section on Statistics in Marketing, Business and Economic Statistics Section, Section on Statistical Learning and Data Science
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Organizer(s): Guannan Wang, College of William and Mary
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Chair(s): Guannan Wang, College of William and Mary
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10:05 AM
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Reduced Rank Autoregressive Models for Matrix Time Series
Rong Chen, Rutgers University
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10:30 AM
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Fast and Fair Simultaneous Confidence Bands for Functional Parameters
Matthew Reimherr, Penn State University
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10:55 AM
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High-Dimensional Sparse Nonlinear Vector Autoregressive Models
Yuefeng Han, Rutgers University; Wei Biao Wu, University of Chicago; Likai Chen, Washington University in St. Louis
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11:20 AM
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Spatiotemporal Dynamics, Nowcasting and Forecasting COVID-19 in the United States
Guannan Wang, College of William and Mary; Lily Wang, Iowa State University; Yueying Wang, Iowa State University
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11:45 AM
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Floor Discussion
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