Zifeng Zhao
Assistant Professor, IT, Analytics and Operations
IT, Analytics, and Operations
574-631-5065
358 Mendoza College of Business
- Biography
- Background
- Publications
- Books
- Grants
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.
Education
Ph D, University of Wisconsin - Madison
MS, University of Wisconsin - Madison
BS, Chinese University of Hong Kong
Areas of Expertise
Change Point Detection
Revenue Management and Learning
Copula and Dependence
Extreme Value Theory
"A composite likelihood-based approach for change-point detection in spatiotemporal processes", (With Ting Fung Ma, Wai Leong Ng, Chun Yip Yau), Journal of the American Statistical Association - Accepted (awaiting publication)
"Enhanced pricing and management of bundled insurance risks with dependence-aware prediction using pair copula construction", (With Peng Shi), Journal of Econometrics - Accepted (awaiting publication)
"Anticipated wait and its effects on consumer choice, pricing, and assortment management", (With Ruxian Wang, Chenxu Ke), Manufacturing and Service Operations Management - Accepted (awaiting publication)
"Functional linear regression with mixed predictors", (With Daren Wang, Yi Yu, Rebecca Willet), Journal of Machine Learning Research, 23, 2022
"Risk analysis via generalized Pareto distributions", (With Yi He, Liang Peng, Dabao Zhang), Journal of Business & Economic Statistics, 40, 2022
"Modeling multivariate time series with copula-linked univariate D-vines", (With Peng Shi, Zhengjun Zhang), Journal of Business & Economic Statistics, 40, 2022
"Modelling the COVID-19 infection trajectory: A piecewise linear quantile trend model", (With Feiyu Jiang, Xiaofeng Shao), Journal of the Royal Statistical Society - Series B, 84, 2022
"Change-point detection for sparse and dense functional data in general dimensions", (With Carlos Padilla, Daren Wang, Yi Yu), Advances in Neural Information Processing Systems
"Segmenting time series via self-normalization", (With Feiyu Jiang, Xiaofeng Shao), Journal of the Royal Statistical Society: Series B
"Statistically and computationally efficient change point localization in regression settings", (With Daren Wang, Kevin Lin, Rebecca Willet), Journal of Machine Learning Research, 22, 2021
"Alternating dynamic programming for multiple epidemic change-point estimation", (With Chun Yip Yau), Journal of Computational and Graphical Statistics, 30
"Regression for copula-linked compound distributions with applications in modeling aggregate insurance claims", (With Peng Shi), Annals of Applied Statistics, 14, 2020
"Knowledge learning of insurance risks using dependence models", (With Shi Peng, Xiaoping Feng), INFORMS Journal on Computing, 33
"Dynamic bivariate Peak over Threshold model for joint tail risk dynamics of financial markets", Journal of Business & Economic Statistics, 39
"Modeling maxima with Autoregressive conditional Frechet model", (With Zhengjun Zhang, Rong Chen), Journal of Econometrics, 207, 2018
"Semi-parametric dynamic max-copula model for multivariate time series", (With Zhengjun Zhang), Journal of the Royal Statistical Society - Series B, 80, 2018
"Inference for multiple change-points in time series via likelihood ratio scan statistics", (With Chunyip Yau), Journal of the Royal Statistical Society - Series B, 78, 2016
"Regressor and disturbance have moments of all order, least squares estimator has none", (With Kenneth West), Statistics & Probability Letters, 115, 2016
"Adjusting for bias in long horizon regressions using R", (With Kenneth West), Handbook of Statistics, 41, 2019
Collaborative Research: Segmentation of Time Series via Self-normalization, NSF, $100,000
Prescriptive Analytics for Multiple-choice Models in Hotel Bookings and Upgrades, Oracle Labs, $30,000