Machine learning and imputation techniques for survey design and missing data

Sun, Aug 6, 4:00 PM

Elizabeth PetragliaChair

Government Statistics Section

6 Presentations

A Machine Learning Approach for Imputation of Missing Race and Ethnicity Information in EHRs

4:05 PM - 4:20 PM

Hui Xie (Centers for Disease Control and Prevention)

Co-authors: Deborah Rolka (Centers for Disease Control and Prevention), Elizabeth Lundeen (CDC), Yu Chen (Centers for Disease Control and Prevention), Rachel Rutkowski (CDC)

Early Season Corn Acreage Estimates in the Presence of Extreme Weather

4:20 PM - 4:35 PM

Jonathon Abernethy (USDA/NASS)

Co-authors: Noemi Guindin (USDA NASS), Kevin Hunt (USDA NASS), Claire Boryan (USDA/NASS), Luca Sartore (National Institute of Statistical Sciences)

Group Quarters Count Imputation for the 2020 Census

4:35 PM - 4:50 PM

Andrew Keller (US Census Bureau)

Machine learning and climate indicators as an approach to enhance operational decisions

4:50 PM - 5:05 PM

Alex Tarter (National Agricultural Statistics Service)

Co-authors: Noemi Guindin (USDA NASS)

On calibrated inverse probability weighting via a machine learning model for incomplete survey data

5:05 PM - 5:20 PM

Joseph Kang (US Census Bureau)

Co-authors: Darcy Morris (U.S. Census Bureau), Patrick Joyce (US Census Bureau), Isaac Dompreh

Optimizing Imputation for an Area Survey

5:20 PM - 5:35 PM

Sean Rhodes (USDA)

Co-authors: Arthur Rosales (USDA/NASS), Luca Sartore (National Institute of Statistical Sciences), Tara Murphy (USDA National Agricultural Statistics Service)

Session participants

Lisa Mirel

Participant