Dr. Tao Wang

Tao Wang is an econometrician who studies issues in modal regression, nonparametric estimation, and machine learning. His current research concentrates on building and developing a broad variety of modal regression models and investigating their statistical inference with potential applications. He also works on applying econometrics and machine learning in an integral manner to improve the performance of econometrics models. He received his PhD from the University of California, Riverside.

Courses:
ECON 366 - Econometrics: Part II
ECON548 - Applied Econometric Modelling

Interests:
Econometrics
Nonparametric Statistics
Machine Learning

Selected Publications:
“Nonlinear Modal Regression for Dependent Data with Application for Predicting COVID-19” (with Ullah, A. and Yao, W.), Journal of the Royal Statistical Society Series A, 2022, forthcoming.

“Modal Regression for Fixed Effects Panel Data” (with Ullah, A. and Yao, W.), Empirical Economics, 2021, 60, 261-308.

“A Machine Learning Strategy for Autism Screening in Toddlers” (with Achenie, L. E. K., Scarpa, A., Factor, R. S., Robins, D. L., and McCrickard, D. S.), Journal of Developmental & Behavioral Pediatrics, 2019, 40 (5), 369-376.