Notes: ∗− Student
On Consistency and Sparsity for High-Dimensional Functional Time Series with an Application to Autoregressions.
Guo, S. and Qiao, X. (2023). Bernoulli, 29(1), 451-472. [pdf].
Sparse Spatial Autoregression by Profiling and Bagging.
Ma, Y., Guo, S. and Wang, H. (2023). Journal of Econometrics, Vol 232, Issue 1, 132-147.
Large Dimensional Portfolio Allocation based on a Mixed Frequency Dynamic Factor Model.
Peng, S.*, Guo, S. and Long, Y. (2022). Econometric Reviews, Volume 41, Issue 5, 539-563.
Large Dynamic Covariance Matrix Estimation with an Application to Portfolio Allocation: A Semiparametric Reproducing Kernel Hilbert Space Approach.
Peng, S.*, Guo, S. and Long, Y. (2022). Journal of Systems Science and Complexity, 35, 1429-1457.
Finite Sample Theory for HighDimensional Functional/Scalar Time Series with Applications.
Fang, Q.*, Guo, S. and Qiao, X. (2022). Electronic Journal of Statistics, Vol 16, 527-591.
Functional Linear Regression: Dependence and Error Contamination.
Chen, C.*, Guo, S. and Qiao, X. (2022). Journal of Business and Economic Statistics, Vol 40, No. 1, 444-457. [pdf]
Better Nonparametric Confidence Intervals via Robust Bias Correction for Quantile Regression.
Guo, S., Han, Y.*, and Wang, Q.* (2021). Stat., Vol 10, Issue 1, e370.
Doubly Functional Graphical Models in High Dimensions.
Qiao, X., Qian, C.*, James, G. and Guo, S. (2020). Biometrika. Vol 107, Issue 2, 415-431.
Strict Stationarity Testing and GLAD Estimation of Double Autoregressive Models.
Guo, S., Li, D. and Li, M. (2019). Journal of Econometrics. Vol 211, Issue 2, 319-337.
Weighted preliminary-summation-based principal component analysis for non-Gaussian processes.
Li, N.*, Guo, S. and Wang. Y. (2019). Control Engineering Practice, Vol 87, 122-132.
Qiao, X., Guo, S. and James, G. (2019). Journal of the American Statistical Association. Vol 114, 525, 211-222.
Double AR model without intercept: an alternative to modeling nonstationarity and heteroscedasticity.
Li, D., Guo, S. and Zhu, K. (2019). Econometric Reviews. Vol 38, No.3, 319-331. [pdf][online]
A dynamic structure for high dimensional covariance matrices and its application in portfolio allocation.
Guo, S., Box, J., and Zhang W. (2017). Journal of the American Statistical Association, 517, V112, 235-253. [pdf] [online]
High dimensional and banded vector autoregressions.
Guo, S., Wang, Y. and Yao, Q. (2016). Biometrika, 103, 889-903. [pdf] [online] [Supplemental]
An overview of semiparametric models in survival analysis.
Guo, S. and Zeng, D. (2014). Journal of Statistical Planning and Inference, V151-152, 1-16. [pdf] [online]
Factor double autoregressive models with application to simultaneous causality testing.
Guo, S., Ling, S. and Zhu, K. (2014). Journal of Statistical Planning and Inference, 148, 82-94. [pdf] [online]
Variance estimation using refitted cross-validation in ultrahigh dimensional regression.
Fan, J., Guo, S. and Hao, N. (2012). Journal of the Royal Statistical Society, Series B , 74,37-65. [pdf] [online]
Marginal regression models with time-varying coefficients for recurrent event data.
Sun, L., Zhou, X. and Guo, S. (2011). Statistics in Medicine , 30, 2265-2277. [pdf] [online]
Least absolute relative error estimation.
Chen, K. , Guo, S., Lin, Y. and Ying, Z. (2010). Journal of the American Statistical Association, 105, 1104-1112. [pdf]] [online]
Global partial likelihood for nonparametric proportional hazards model.
Chen, K., Guo, S., Sun, L. and Wang, J. L. (2010). Journal of the American Statistical Association, 105, 750-760. [pdf] [online]
Marginal regression model with time-varying coefficients for panel data.
Sun, L., Guo, S. and Chen, M. (2009). Communications in Statistics: Theory and Methods, 38, 1241-1261. [pdf] [online]
On locally weighted estimation and hypothesis testing on varying coefficient models with missing covariates.
Wong, H., Guo, S., Chen, M., and Ip, W. C. (2009). Journal of Statistical Planning and Inference, 139}, 2933-2951. [pdf] [online]
Precise asymptotics of error variance estimator in partially linear models.
Guo, S., Chen, M., and Liu, F. (2009). Acta. Math. Appl. Sinica. (English Series), 24, 59-74. [pdf] [online]
Nonparametric smoothing in semiparametric additive hazards models: to use Kernel, Sieve or their Aggregations?
Guo, S., Song, R. and Zhou, H. (2018). Manuscript. [pdf]
Valid inference of semiparametric estimators in high dimensions.
Large Dynamic Covariance Matrix Modeling via Locally/Globally Adaptive Threholdings
• Inference for high dimensional linear regression with nonsparse confounders
• Inference for high dimensional linear regression with correlated errors
Predicting survival probabilities with high dimensional predictors and double robustness.
Seminar talk on 5th April,2016 at UC-Riverside, USA;
Seminar talk on 8th April 2016 at University of Southern California, USA.