Air Quality Assessment  

Develop statistical methods to measure the effectiveness of the air pollution mitigation strategies based on objective air quality measures that remove the meteorological confounding.

1Shuyi Zhang, Song Xi Chen, Bin Guo, Hengfang Wang, Wei Lin (2020) Regional Air-Quality Assessment That Adjusts for Meteorological Confounding​, Science China Mathematics (in Chinese) 50, 527-558.[pdf] 
2Ziping Xu, Song Xi Chen, Xiaoqing Wu(2020) Meteorological Change and Impacts on Air Pollution Results from North China, Journal of Geophysics Research-Atmosphere.[pdf] 
3Shuyi Zhang, Song Xi Chen, Bin Guo, Hengfang Wang, Wei Lin (2020) Regional Air-Quality Assessment That Adjusts for Meteorological Confounding​, Science China Mathematics, to appear. [pdf] 
4Lei Chen, Bin Guo, Jiasheng Huang, Hengfang Wang, Shuyi Zhang and Song Xi Chen (2018). Assessing Air-Quality in Beijing-Tianjin-Hebei Region: the Method and Mixed Tales of PM2.5 and O3. Atmospheric Environment 193 (2018) 290–301.[pdf] 
5Zhang S, Guo B, Dong A, He J, Xu Z, Chen SX. 2017 Cautionary tales on air-quality improvement in Beijing. Proc. R. Soc. A 20170457.[pdf] 
6 Liang, X., Li, S., Zhang, SY, Huang, H. and S.X. Chen (2016). PM2.5 Data Reliability, Consistency and  Air Quality Assessment in Five Chinese Cities,  Journal of Geophysical Research¡ªAtmosphere,  121, 10,220_10,236.[pdf] Data
7 Liang, X., T. Zou, B. Guo, S. Li, H. Zhang, S. Zhang, H. Huang and S. X. Chen. (2015). Assessing Beijing's PM2.5 Pollution: Severity, Weather Impact, APEC and Winter Heating, Proceedings of the Royal Society A, 471, 20150257.[pdf] Data

High Dimensional Statistical Inference  

Development methods applicable to a new norm of data: the dimension of the data is much larger than the number of sample points as commonly encountered in genetic and bio-medical studies, brain imaging and social and economic analyses.


1 Mao, X-J., Wong, R. K-W and Chen, S. X. (2020) Matrix Completion under Low-Rank Missing Mechanism, Statistica Sinica, to appear.[pdf] 
2Mao, X., Chen, SX and Wong, R.(2019) Matrix Completion with Covariate Information, Journal of the American Statistical Association, 2019, VOL. 114, NO. 525, 198–210, Theory and Methods[pdf] 
3S.X. Chen, J. Li and P.-S. Zhong (2019), Two-Sample and ANOVA Tests for High Dimensional Means, The Annals of Statistics, Vol. 47, No. 3, 1443-1474.[pdf] Code 
4Qiu, Y., Chen, S.X. and Nettleton, D.(2018)Detecting Rare and Faint Signals via Thresholding Maximum Likelihood Estimators, Annals of Statistics, 46, 895-923. [pdf] 
5Jing He and Song Xi Chen (2018). High-Dimensional Two-Sample Covariance Matrix Testing via Super-Diagonals. Statistica Sinica 28 (2018), 2671-2696[pdf] 
6 Guo, B. and S.X.Chen (2016). Tests for High Dimensional Generalized Linear Models. Journal of the Royal Statistical Society, Series B. 78, 1079–1102.[pdf] Code 
7 Peng, LH, S.X. Chen and W, Zhou (2016) More Powerful Tests for Sparse High-Dimensional Covariances Matrices, Journal of Multivariate Analysis,  149, 124-143.[pdf] Code 
8 He, J. and S. X. Chen (2016) Testing Super-Diagonal Structure in High Dimensional Covariance Matrices, Journal of Econometrics, 194,  283-297[pdf] Code 
9 Chang, J-Y, Chen, S.X. and X. Chen (2015). High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data. Journal of Econometrics, 185, 283-304.[pdf] 
10 Qiu, Y-M and Chen, S.X. (2015)  Band Width Selection for High Dimensional Covariance Matrix Estimation. Journal of the American Statistical Association, 110, 1160-1174.[pdf] Code 
11 Zhong, P-S, Chen, S. X. and Xu M. (2013). Tests alternative to higher criticism for high dimensional means under sparsity and column-wise dependence, Annals of Statistics, 41, 2820-2851.[pdf] Code 
12 Qiu, Y-M and Chen, S. X. (2012). Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation, TheAnnals of Statistics, 40, 1285-1314.[pdf] Code 
13 Li, J. and S. X. Chen (2012). Two Sample Tests for High Dimensional Covariance Matrices, The Annals of Statistics, 40, 908-940.[pdf] Code 
14 P-S Zhong and S. X. Chen (2011). Tests for High Dimensional Regression Coefficients with Factorial Designs. Journal of the American Statistical Association, 106, 260-274.[pdf] Code 
15 Chen, S.X., Zhang, L-X. and P-S Zhong (2010). Testing high dimensional covariance matrices. Journal of the American Statistical Association, 105, 810-819.[pdf] Code 
16 Chen, S. X. and Y. L. Qin (2010). A two sample test for high dimensional data with application to gene-set testing, The Annals of Statistics, 38, 808-835.[pdf] Code 
17 Chen, S. X., L. Peng and Y-L, Qin (2009). Effects of Data Dimension on Empirical Likelihood, Biometrika, 96, 711_722.[pdf] 

Econometrics  

Modeling and analyses of Chinese economic data and general econometric methods.

1Tao Zou & Song Xi Chen (2017) Enhancing Estimation for Interest Rate Diffusion Models With Bond Prices, Journal of Business & Economic Statistics, 35:3, 486-498[pdf] 
2 Wang, Y., Tu, Y-D and S. X. Chen (2016) Improving inflation prediction with the quantity theory. Economics Letters, 149, 112-115.[pdf] 
3 He, J. and S. X. Chen (2016) Testing Super-Diagonal Structure in High Dimensional Covariance Matrices, Journal of Econometrics, 194,  283-297[pdf] Code 
4 Chen, S.X., Lei, L.-H. and Tu, Y-D (2016). Functional Coefficient Moving Average Models with applications to forecasting Chinese CPI, Statistica Sinica, 26, 1649-1672. [pdf] 
5 Chang, J-Y, Chen, S.X. and X. Chen (2015). High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data. Journal of Econometrics, 185, 283-304.[pdf] 
6 Chen, S.X. and Z. Xu (2014). On Implied Volatility for Options - Some Reasons to Smile and More to Correct. Journal of Econometrics, 179, 1-15.[pdf] 
7 Chen, S.X. and Z. Xu (2013). On smoothing estimation for seasonal time series with long cycles, Statistics and Its Interface, 6, 435-447.[pdf] 
8 J. Chang and S.X. Chen (2011). On the approximate maximum likelihood estimation for diffusion processes. The Annals of Statistics, 39, 2820-2851.[pdf] 
9 Chen, S.X. and J. Gao (2011).  Simultaneous Specification Test for the Mean and Variance Structures for Nonlinear Time Series regression. Econometric Theory, 27, 2011, 792_843.[pdf] 
10 Chen, S. X., Delaigle, A. and Hall, P. (2010). Nonparametric estimation for a class of levy processes, Journal of Econometrics, 157, 257-271.[pdf] 
11 C. Y. Tang and S. X. Chen (2009). Parameter estimation and bias correction for diffusion processes. Journal of Econometrics, 149, 65-81.[pdf] 
12Chen, S.X: (2008). Nonparametric Estimation of Expected Shortfall. Journal of Financial Econometrics, 6, 87-107.[pdf] 
13  Chen, S.X. and H.-J., Cui (2007). On the second order properties of empirical likelihood with moment restrictions , Journal of Econometrics, 141, 492-516.[pdf] 
14Chen, S.X. and J. Gao (2007). An Adaptive Empirical Likelihood Test For Time Series Models, paper, full report, Journal of Econometrics, 141, 950-972.[pdf] 
15Chen, S. X. and Tang, C. Y. (2005). Nonparametric Inference of Value at Risk for dependent Financial Returns. Journal of Financial Econometrics, 3, 227-255.[pdf]