Conference Program Home
My Program
All Times EDT
Legend:
CC = Walter E. Washington Convention Center M = Marriott Marquis Washington, DC
* = applied session ! = JSM meeting theme
Activity Details
6 * !
Sun, 8/7/2022,
2:00 PM -
3:50 PM
CC-202B
JSSAM Special Issue: Privacy, Confidentiality, and Disclosure Protection — Invited Papers
Journal of Survey Statistics and Methodology , Caucus for Women in Statistics
Organizer(s): Katherine J. Thompson, US Census Bureau
Chair(s): Katherine J. Thompson, US Census Bureau
2:05 PM
A Semiparametric Multiple Imputation Approach to Fully Synthetic Data for Complex Surveys
Mandi Yu, National Cancer Institute; Yulei He, National Center for Health Statistics; Trivellore Eachambadi Raghunathan, University of Michigan
2:30 PM
Protecting the Identity of Participants in Qualitative Research
Presentation
Joanne Pascale, U.S. Census Bureau ; Fane Lineback, U.S. Census Bureau; Nancy Bates, U.S. Census Bureau; Paul Beatty, U.S. Census Bureau
2:55 PM
Overview of the Special Issue
Anne-Sophie Charest, Université Laval
3:20 PM
Discussant: Jerome P. Reiter, Duke University
3:40 PM
Floor Discussion
311 * !
Tue, 8/9/2022,
2:00 PM -
3:50 PM
CC-143B
Does Missing Data Affect Outcomes Examined Using Nationally Representative Survey Databases? A Comparison of Traditional and Data Science Approaches — Invited Papers
Survey Research Methods Section , Journal of Survey Statistics and Methodology, Academy for Health Services Research and Health Policy
Organizer(s): Parul Agarwal, Icahn School of Medicine at Mount Sinai
Chair(s): Yan Ma, George Washington University
2:05 PM
A Review of Missing Data Approaches and Practices in Large Nationally Representative Survey Databases
Parul Agarwal, Icahn School of Medicine at Mount Sinai
2:30 PM
Data-Driven Methods for Missing Data Imputation in Health Disparities Research
Yuhao Zhang, George Washington University ; Yuxiao Huang , George Washington University ; Yan Ma, George Washington University
2:55 PM
Making Use of Summary Information from Large Databases Without Access to Their Individual Data
Peisong Han, University of Michigan
3:20 PM
Are Deep Learning Models Superior for Missing Data Imputation in Large Surveys? Evidence from an Empirical Comparison
OLANREWAJU MICHAEL AKANDE, Duke University ; ZHENHUA WANG, University of Missouri; Jason Poulos, Harvard Medical School; Fan Li, Duke University
3:45 PM
Floor Discussion