Dr. Tao Wang

Bio

Tao Wang is an Econometrician who studies issues in Nonparametric Estimation and Machine Learning. His current research concentrates on building and developing a broad variety of modal and mode-based regression models and investigating their statistical inference with possible 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 in June 2022.

Interests

• Econometrics
• Nonparametric Statistics
• Machine Learning

Courses

• ECON 365, Econometrics: Part I
• ECON 366, Econometrics: Part II
• ECON 548, Applied Econometric Modelling

Selected Publications

• Wang, T. (2024+). Distributed Learning for Kernel Mode-Based Regression. The Canadian Journal of Statistics, accepted.

• Wang, T. (2023+). Parametric Modal Regression with Autocorrelated Error Process. Statistica Sinica, available online.
• Wang, T. (2024). Nonlinear Kernel Mode-Based Regression for Dependent Data. Journal of Time Series Analysis, 45 (2), 189-213.
• Wang, T. (2024). Nonparametric Estimator for Conditional Mode with Parametric Features. Oxford Bulletin of Economics and Statistics, 86 (1), 44-73.
• Ullah, A., Wang, T., and Yao, W. (2023). Semiparametric Partially Linear Varying Coefficient Modal Regression. Journal of Econometrics, 235 (2), 1001-1026.
• Ullah, A., Wang, T., and Yao, W. (2022). Nonlinear Modal Regression for Dependent Data with Application for Predicting COVID-19. Journal of the Royal Statistical Society Series A, 185 (3), 1424-1453.