• Assistant Professor of Statistics
• Ph.D., University of Chicago, 2007
• Personal web site (under construction)

Summary of research interests

Dr. Zhao’s main research interests lie in statistical inferences for dependent data (for example, time series), robust estimation, and asymptotic theories for stochastic processes.

Many existing statistical methods have been developed for independent data and may not be applicable when dependence is present. Dr. Zhao has been developing parametric and nonparametric techniques for linear and nonlinear time series that may exhibit short-range or long-range dependence. Applications of his research include drift and volatility functions estimation in stochastic diffusion models, model validations for financial time series, and modeling global warming temperature data among others.

Dr. Zhao is also working on robust methods.  Many financial datasets exhibit heavy tails, and the classical Least-Square type methods may not be applicable. For such heavy-tailed data, Least-Absolute-Deviation and quantile regression type methods are good alternatives.
  
Representative Publications:

Wu, W.B. and Zhao, Z. (2007).  Inference of trends in time series. Journal of the Royal Statistical Society Series B, 69: 391-410.

Zhao, Z. and Wu, W.B. (2007).  Asymptotic theory for curve-crossing analysis. Stochastic Processes and their Applications, 117: 862-877.

Last updated: August 20, 2007