个人简介
现为中国人民大学统计与大数据研究院长聘副教授。2003年本科毕业于山东师范大学，2008年获得中国科学院数学与系统科学研究院理学博士学位。博士毕业后留中国科学院数学与系统科学研究院工作，助理研究员，任期至2016年。2009年-2010年赴美国普林斯顿大学运筹与金融工程系博士后研究，做高维数据分析方面的研究工作，并于2014-2016年在英国伦敦经济学院统计系做博士后研究，做大维时间序列建模方面的研究。
研究领域
统计学习；非参数及半参数统计建模；生存分析及函数型数据分析等
近期论文
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1. Guo, S. and Qiao, X. (2022). On Consistency and Sparsity for High-Dimensional Functional Time Series with an Application to Autoregressions. Accepted by Bernoulli.
2. Fang, Q.∗, Guo, S. and Qiao, X. (2021). Finite Sample Theory for HighDimensional Functional/Scalar Time Series with Applications. Accepted by Electronic Journal of Statistics.
3. Peng. S.∗, Guo, S. and Long. Y. (2021). Large Dimensional Portfolio Allocation based on a Mixed Frequency Dynamic Factor Model. Accepted by Econometric Reviews.
4. Peng. S.∗, Guo, S. and Long. Y. (2021). Large Dynamic Covariance Matrix Estimation with an Application to Portfolio Allocation: A Semiparametric Reproducing Kernel Hilbert Space Approach.
Accepted by Journal of Systems Science & Complexity.
5. Ma, Y., Guo, S. and Wang, H. (2020). Sparse Spatial Autoregression by Profiling and Bagging. Accepted by Journal of Econometrics.
6. Chen, C., Guo, S. and Qiao, X. (2020). Functional linear regression: dependence and error contamination. Accepted by Journal of Business and Economic Statistics.
7. Guo, S., Han, Y., and Wang, Q. (2021). Better Nonparametric Confidence Intervals via Robust Bias Correction for Quantile Regression. Accepted by Stat Journal.
8. Qiao, X., Qian, C., James, G. and Guo, S. (2020). Doubly Functional Graphical Models in High Dimensions. Biometrika. Vol 107, Issue 2, 415-431.
9. Guo, S., Li, D. and Li, M. (2019). Strict stationarity testing and GLAD estimation of double autoregressive models. Journal of Econometrics. Vol 211, Issue 2, 319-337.
10. Li, N.*, Guo, S. and Wang. Y. (2019). Weighted Preliminary-Summation-Based Principal Component Analysis for Non-Gaussian Processes. Control Engineering Practice, Vol 87, 122-132.
11. Qiao, X., Guo, S. and James, G. (2019). Functional graphical models. Journal of the American Statistical Association. Vol 114, 525, 211-222.
12. Li, D., Guo, S. and Zhu, K. (2019). Double AR model without intercept: an alternative to modeling nonstationarity and heteroscedasticity. Econometric Reviews, Vol 38, No.3, 319-331.
13. Guo, S., Box, J. and Zhang, W. (2017). A dynamic structure for high dimensional covariance matrices and its application in portfolio allocation. Journal of the American Statistical Association, 517, Vol 112, 235-253.
14. Guo, S., Wang, Y. and Yao, Q. (2016). High dimensional and banded vector autoregressions. Biometrika, 103, 889-903.
15. Guo, S. and Zeng, D. (2014). An overview of semiparametric models in survival analysis. (Invited article). Journal of Statistical Planning and Inference. 151-152, 1-16.
16. Guo, S., Ling, S. and Zhu, K. (2014). Factor double autoregressive models with application to simultaneous causality testing. Journal of Statistical Planning and Inference. 148, 82-94.
17. Fan, J., Guo, S. and Hao, N. (2012). Variance estimation using refitted cross-validation in ultra-high dimensional regression. Journal of the Royal Statistical Society, Series B, 74, 37-65.
18. Sun, L., Zhou, X. and Guo, S. (2011). Marginal regression models with time-varying coefficients for recurrent event data. Statistics in Medicine, 30, 2265-2277.
19. Chen, K., Guo, S., Lin, Y. and Ying, Z. (2010). Least absolute relative error estimation. Journal of the American Statistical Association, 105, 1104-1112.
20. Chen, K., Guo, S., Sun L. and Wang, J. L. (2010). Global partial likelihood for nonparametric proportional hazards model. Journal of the American Statistical Association, 105, 750-760.
21. Sun, L., Guo, S. and Chen, M. (2009). Marginal regression model with time-varying coefficients for panel data. Communications in Statistics: Theory and Methods, 38, 1241-1261.
22. Wong, H., Guo, S., Chen, M. and Ip, W.C. (2009). On locally weighted estimation and hypothesis testing on varying coefficient models with missing covariates. Journal of Statistical Planning and Inference, 139, 2933-2951.
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