mgm高梅美线路: 师资团队Faculty









Technical Reports:

1. Fang, Q.?, Guo, S. and Qiao, X. (2022). Adaptive Thresholding for High Dimensional Covariance Function. Revision in Journal of American Statistical Association.

2. Guo, S., Qiao, X. and Wang, Q. (2022). Factor Modelling for High Dimensional Functional Time Series. Submitted.


Notes: ?- Student.

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.