Abstract:
Incorporating information from several sources is a fundamental problem in statistics. In statistical agencies, the desire to combine information from different sources to obtain an improved official statistic is increasing and survey data integration becomes an emerging area of research. In this workshop, we review the current state-of-the-art methods for survey data integration and discuss some future research direction. Prediction approach and propensity score weighting approaches are two main techniques for survey data integration. Some advanced topics, such as projection in Hilbert space and multi-level modeling, will also be covered.
About the Speaker:
Jae-kwang Kim is a professor in Statistics department at Iowa State University (ISU) . He is a fellow of ASA and IMS and named to a Liberal Arts and Sciences Dean's Professor from the college of Liberal Arts and Sciences at Iowa State University. His main research interest lies in survey sampling and statistical analysis with missing data, and related topics in measurement error, multi-level models, and data integration. His recent work focuses on machine learning topics such as function estimation under reproducing kernel Hilbert space.
Personal Homepage: https://www.stat.iastate.edu/people/jae-kwang-kim
Schedule:
Oct 9, 2023, 2:00-5:30 p.m
Part 1: Survey data integration introduction, two-phase sampling
Part 2: Survey data integration: Prediction approach
Oct 10, 2023, 2:00-5:30 p.m
Part 3: Survey data integration: weighting approach
Part 4: Survey data integration: advanced topics.
Your participation is warmly welcomed!
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