A Bayesian framework of combining multiple data sources for small area estimation

Music City Center 

Small area estimation often relies on model-based approaches to stabilize estimates of subgroups with small sample sizes. The model-based approaches can be hierarchical models or introduce prior distributions in a Bayesian paradigm to borrow information across subgroups. Rich literature work has made important contributions to SAE methods, especially with applications to complex sample surveys. However, due to recent data collection challenges, survey data alone cannot meet analytic demands. Combining multiple data sources has become a research priority. SAE methods need to account for data collection tailored to each data source and integrate all relevant information to improve inference. We consider a few scenarios, where multiple data sources collect different measure components and participant groups, and develop a Bayesian SAE framework. We will compare with alternatives and use simulation and application studies to illustrate the improvement.

Small area estimation

Data integration

Bayesian models