Jarod Lee is interested in solving practical problems with real world impact. His dissertation focuses on developing a class of computationally efficient models for longitudinal and multilevel data. The modelling framework are applied in a variety of medical, marketing and social sciences studies, and have important implications for privacy preservation in large-scale databases.
Discrete Choice Models
Generalized Linear Mixed Models
Longitudinal Data Analysis
Small Area Estimation
Bachelor of Science (Hons) in Mathematics, University of Technology Sydney
Bachelor of Actuarial Studies, Australian National University
Lee, J., Brown J., Ryan L. M., & Tzavidis N.
(2017). Exploring the Robustness of Log-Gamma vs. Normal for Random Effect Distributions: The Case of COunt Data. International Conference on Robust Statistics.