Contact Information
725 S. Wright St.
M/C 374
Champaign, IL 61820
Biography
2016 – Present: Full Professor, Department of Statistics, University of Illinois at Urbana-Champaign
2016 – Present: Ph.D. Program Director, Department of Statistics
2006 – 2016: Assistant/Associate Professor, Department of Statistics, University of Illinois at Urbana-Champaign
Research Interests
Long memory and nonlinear time series
Spatial statistics
Spectral analysis
Environmental Statistics
Econometrics
Functional data analysis
High dimensional data analysis
Resampling methods
Time series analysis
Applications in atmospheric science, economics, finance and neuroscience
Education
PhD, Statistics, University of Chicago, 2006
Grants
NSF-CMG0724752 (co-PI)
NSF-DMS0804937 (PI)
NSF-DMS1104545 (PI)
NSF-DMS1407037 (PI)
NSF-DMS1607489 (PI)
Research Board at UIUC
ABinBev
Jump
Awards and Honors
The Tjalling C. Koopmans Econometric Theory Prize, 2009 (with Wei Biao Wu)
Arnold O. Beckman Research Award, UIUC 2010 Econometric Theory Multa Scripist Award, 2011.
Centennial Scholar, College of Liberal Arts and Sciences, 2013-2016
External Links
Recent Publications
ZHU, CHANGBO., ZHANG, XIANYANG., YAO, SHUN., & SHAO, XIAOFENG. (2021). Distance-based and rkhs-based dependence metrics in high dimension. Annals of Statistics, 48(6), 3366-3394. https://doi.org/10.1214/19-AOS1934
Jiang, F., Zhao, Z., & Shao, X. (Accepted/In press). Time series analysis of COVID-19 infection curve: A change-point perspective. Journal of Econometrics. https://doi.org/10.1016/j.jeconom.2020.07.039
Lee, C. E., Zhang, X., & Shao, X. (2020). Testing conditional mean independence for functional data. Biometrika, 107(2), 331-346. https://doi.org/10.1093/biomet/asz070
Lee, C. E., & Shao, X. (2020). Volatility Martingale Difference Divergence Matrix and Its Application to Dimension Reduction for Multivariate Volatility. Journal of Business and Economic Statistics, 38(1), 80-92. https://doi.org/10.1080/07350015.2018.1458621
Wang, R., & Shao, X. (2020). Hypothesis testing for high-dimensional time series via self-normalization. Annals of Statistics, 48(5), 2728-2758. https://doi.org/10.1214/19-AOS1904