Zifeng Zhao

Zifeng Zhao is an Assistant Professor of Business Analytics at the Mendoza College of Business. His research focuses on solving business analytics problems via statistics and machine learning. His interests include developing copula-based statistical models for multivariate time series and multivariate longitudinal data, designing extreme value theory (EVT)-based models for financial risk monitoring, and building efficient staistical algorithms for change-point detection and large-scale forecasting. His research has been applied to areas like financial risk management, portfolio optimization, insurance risk classification and pricing, and web search traffic forecasting. Zhao has a PhD in Statistics and an MS degree in Machine Learning from the University of Wisconsin-Madison, and a BS degree in Financial Risk Management from the Chinese University of Hong Kong.

Research interests:

• change-point analysis
• copula modeling
• functional data analysis
• extreme value theory
• time series modeling
• business analytics

Publications

• D. Wang, Z. Zhao, K. Lin, R. Willett (2021+) Statistically and computationally efficient change point localization in regression settings, Journal of Machine Learning Research. (Accepted)
• F. Jiang, Z. Zhao, X. Shao (2021+) Modelling the COVID-19 infection trajectory: A piecewise linear quantile trend model, Journal of the Royal Statistical Society - Series B, with discussion. (Accepted)
• Y. He, L. Peng, D. Zhang, Z. Zhao (2021) Risk analysis via generalized Pareto distributions, Journal of Business & Economic Statistics. (In press)
• Z. Zhao, C.Y. Yau (2021) Alternating dynamic programming for multiple epidemic change-point estimation, Journal of Computational and Graphical Statistics. - paper
• Z. Zhao, P. Shi, Z. Zhang (2021) Modeling multivariate time series with copula-linked univariate D-vines, Journal of Business & Economic Statistics. (In press) -
• F. Jiang, Z. Zhao, X. Shao (2020) Time series analysis of COVID-19 infection curve: a change-point perspective, Journal of Econometrics.
• Z. Zhao, P. Shi, X. Feng (2020) Knowledge learning of insurance risks using dependence models, INFORMS Journal on Computing.
• Z. Zhao (2020) Dynamic bivariate peak over threshold model for joint tail risk dynamics of financial markets, Journal of Business & Economic Statistics.
• P. Shi, Z. Zhao (2020) Regression for copula-linked compound distributions with applications in modeling aggregate insurance claims, Annals of Applied Statistics. -
• Z. Zhao, Z. Zhang, R. Chen (2018) Modeling maxima with autoregressive conditional Frechet model, Journal of Econometrics.
• Z. Zhao, Z. Zhang (2018) Semi-parametric dynamic max-copula model for multivariate time series, Journal of the Royal Statistical Society - Series B.
• C.Y. Yau, Z. Zhao (2016) Inference for multiple change-points in time series via likelihood ratio scan statistics, Journal of the Royal Statistical Society - Series B. -
• K. West, Z. Zhao (2016) Regressor and disturbance have moments of all order, least squares estimator has none, Statistics & Probability Letters.

Grants

• Collaborative Research: Segmentation of Time Series via Self-normalization, NSF, $100,000