Speaker: Jaewoo Park Location: B105 Date and Time:
About the Speaker: Dr. Jaewoo Park
- (Current) Assistant Professor, Department of Applied Statistics, Yonsei University
- Ph.D. in Statistics, The Pennsylvania State University
- BA in Applied Statistics, Economics, Yonsei University
Spatial regressions are widely used to study relationships between spatial variables in many scientific domains, such as ecology, epidemiology, and sociology. For a given spatial latent process, hierarchical structures are defined for building complex high-dimensional models from simple and low-dimensional building blocks. Bayesian approaches are popular for hierarchical spatial models due to their computational and inferential advantages. In this talk, I will review the Bayesian framework for hierarchical spatial regression models. I will show how such models can be applied to real datasets, such as confirmed COVID-19 cases in the United States.