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.





空气质量评估平台


Reports

北京城区2010-2014年PM 2.5 污染状况研究

中国五城市空气污染状况之统计学分析

北京地区 2013-2016 年区域污染状况评估

京津冀 2013-2016 年区域污染状况评估

“2+31”城市 2013-2017 年区域污染状况评估

“2+43”城市2013-2018 年区域污染状况评估

“2+66”城市2013-2019年区域污染状况评估

Publications

1 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] 
2 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:Atmosphere,  121, 10,220_10,236.[pdf] 
3Zhang 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] 
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] 
5Li, HB, Wu, JW., Wang, AX, Li, X, Chen, SX, Wang, TQ, Amsalu, E., Gao, Q., Luo, YX, Yang, XH., Wang, W, Guo, J., Guo, YM, Guo, XH. (2018). Effects of ambient carbon monoxide on daily hospitalizations for cardiovascular disease: a time-stratified case-crossover study of 460,938 cases in Beijing, China from 2013 to 2017, ENVIRONMENTAL HEALTH, 17:82.[pdf] 
6Ziping Xu, Song Xi Chen, Xiaoqing Wu (2020) Meteorological Change and Impacts on Air Pollution Results from North China, Journal of Geophysics Research-Atmosphere, 125 (16), e2020JD032423.[pdf] 
7张澍一，陈松蹊，郭斌，王恒放，林伟(2020)气象调整下的区域空气质量评估(Regional Air-Quality Assessment That Adjusts for Meteorological Confounding​)，中国科学：数学(Science China Mathematics)，第50卷第4期527~558.[pdf] 
8Yating Wan, Minya Xu, Hui Huang and Song Xi Chen(2020) A spatio-temporal model for the analysis and prediction of fine particulate matter concentration in Beijing, Enviromentrics, e2648[pdf] 
9Wu, H., Zheng, X., Zhu, J., Lin, W., Zheng, H., Chen, X., Wang, W., Wang, Z., and S. X. Chen (2020). Improving PM2.5 forecasts in China suing an initial error transport model, Environmental Science and Technology, 54(17), 10493-10501.[pdf] 
10Li, S., Liu, R. and Chen, S.X. (2021) Radiative Effects of Particular Matters on Ozone Pollution in Six Northern China Cities, revised for Journal of Geophysical Research[pdf] 
11Zheng, X-Y. and Chen, S.X. (2021) Dynamic Synthetic Control Method for Evaluating Effects of Air Pollution Alerts.[pdf] 
12Zheng, X-Y., Guo, B., He, J. and Chen, S.X. (2021) Effects of COVID-19 Control Measures on Air Quality in North China (Invited paper）, Environmetrics , to appear[pdf] 
13吴煌坚，林伟，孔磊，唐晓，王威，王自发，陈松蹊 (2021) 一种基于集合最优插值的排放源快速反演方法, 《气候与环境研究》, 接受。[pdf] 
14Yuru Zhu, Yinshuang Liang, Song Xi Chen(2021) Assessing Local Emission for Air Pollution via Data Experiments, Atmospheric Environment[pdf] 

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.


Publications

1 Chen, S. X., L. Peng and Y-L, Qin (2009). Effects of Data Dimension on Empirical Likelihood, Biometrika, 96, 711_722.[pdf] 
2 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 
3 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 
4 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 
5 Li, J. and S. X. Chen (2012). Two Sample Tests for High Dimensional Covariance Matrices, The Annals of Statistics, 40, 908-940.[pdf] Code 
6 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 
7 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 
8 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 
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 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 
11 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 
12 He, J. and S. X. Chen (2016) Testing Super-Diagonal Structure in High Dimensional Covariance Matrices, Journal of Econometrics, 194,  283-297[pdf] Code 
13Jing He and Song Xi Chen (2018). High-Dimensional Two-Sample Covariance Matrix Testing via Super-Diagonals. Statistica Sinica 28 (2018), 2671-2696[pdf] 
14Qiu, 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] 
15S.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 
16Mao, 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] 
17 Mao, X-J., Wong, R. K-W and Chen, S. X. (2021) Matrix Completion under Low-Rank Missing Mechanism, Statistica Sinica, to appear.[pdf] 
18Chang, J-Y., Chen, S.X., Tang, C-Y. and Wu, T-T (2021) High-dimensional empirical likelihood inference, Biometrika, to appear.[pdf] 

Empirical Likelihood  

参数似然是传统统计学的核心方法， 极大似然估计和似然比检验是统计学的基本方法。参数似然具有两个标志性结果。一个是Wilks定理，即参数似然比具有渐进WechatIMG363.png分布。另一个是以英国皇家院士Bartlett命名的巴特莱特调整。参数似然的局限是依赖于过强的模型设定。

经验似然是由斯坦福大学Art Owen在1988年提出的，在比参数似然更弱的模型设定下构造似然函数的方法，英国皇家、美澳科学院院士Peter Hall曾指出：“经验似然是近年提出的非参数方法的有力竞争对手，会成为计算密集型统计方法的重要一员”。陈松蹊在几个重要框架下建立了经验似然的一阶Wilks定理和二阶巴特莱特调整，为经验似然成为基本的非参数统计方法贡献了关键性结果。


Publications

1 Chen, S.X. and Hall, P. (1993). Smoothed empirical likelihood confidence intervals for quantiles. Ann. Of Statistics, 21,1166-1181.[pdf] 
2 Chen, S.X. (1993). On the coverage accuracy of empirical likelihood confidence regions for linear regression model. Annals of Institute of Statistical Mathematics, 45, 621-637.[pdf] 
3Chen, S.X. (1994). Comparing empirical likelihood and bootstrap hypothesis tests. J. Mult. Anal, 51, 277-293.[pdf] 
4Chen, S.X. (1994). Empirical likelihood confidence intervals for linear regression coefficients. J. Mult. Anal. 49, 24-40.[pdf] 
5Chen, S.X. and Hall, P. (1994). On the calculation of standard error for quotation in confidence statements. Statistics and Probability Letters,19,147-151.[pdf] 
6 Chen, S.X. (1996). Empirical likelihood confidence intervals for nonparametric density estimation. Biometrika, 83, 329-341.[pdf] 
7 Chen, S.X. (1997). Empirical likelihood-based kernel density estimation. Aust. J. Statist. , 39,47-56[pdf] 
8Brown, B. M. and Chen, S. X. (1998). Combined Empirical Likelihood. Ann. Inst. Statist. Math, 50, 697-714.[pdf] 
9Chen, S. X. and Qin, Yong Song (2000). Empirical Likelihood confidence interval for a local linear smoother. Biometrika, 87, 946-953. [pdf] 
10 Chen, S. X., Hardle, W. and Kleinow, T. (2002). An empirical likelihood goodness-of-fit test for diffusions. Applied quantitative finance, 259--281, Springer, Berlin.
11 Chen, S. X. and Qin, Y-S. (2003). Coverage accuracy of confidence intervals in nonparametric regression. Acta Math. Appl. Sin. Engl. Ser.19,387--396.[pdf] 
12Chen, S. X., D. H. Y. Leung and Qin, J. (2003). Information Recovery in a Study with Surrogate Endpoints.  Journal of the American Statistical Association, 98,1052--1062.[pdf] 
13Chen, S. X. and Qin, J. (2003). Empirical likelihood based confidence intervals for data with possible zero observations. Statistics and Probability Letters, 65, 29-37. [pdf] 
14Chen, S. X., Haredle, W. and Li, M. (2003). An empirical likelihood goodness-of-fit test for time series. Journal of The Royal Statistical Society, Series B, 65, 663-678.[pdf] 
15 Chen, S. X and Cui, H-J. (2003). An extended empirical likelihood for generalized linear models. Statistica Sinica, 13, 69-81. [pdf] 
16Cui, H-J and Chen, S.X. (2003).Empirical likelihood confidence regions for parameter in the errors-in-variables models, Journal of Multivariate Analysis, 84 (1), 101-115.[pdf] 
17 Chen, S.X. and H.-J., Cui (2006). On Bartlett Correction of Empirical Likelihood in the Presence of Nuisance Parameters, Biometrika, 93, 215-220.[pdf] 
18 Chen, S.X. and Qin, J. (2006). An Empirical likelihood Method in Mixture Models with Incomplete Classifications, Statistica Sinica,16, 1101-1115.[pdf] 
19Chen, 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] 
20  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] 
21Chen, S. X., Leung, D. Y. H. and J. Qin (2008). Improving Semiparametric Estimation Using Surrogate Data. Journal of the Royal Statistical Society, Series B, 70, 803-823.[pdf] 
22Chen, S.X., J. Gao and C. Y. Tang (2008). A Test for Model Specification of Diffusion Processes. The Annals of Statistics, 36, 167-198.[pdf] 
23 Chan, N-H, Chen, S.X., Peng, L. and C. L. Yu (2009). Empirical Likelihood Methods Based on Characteristic Functions with Applications to L'evy Processes. Journal of the American Statistical Association, 104, 1621-1630.[pdf] 
24 Chen, S. X. and I. Van Keilegom (2009). A review on empirical likelihood for regressions (with discussions), Test, 3, 415-447 .[pdf] 
25 Chen, S. X. and Van Keilegom, I. (2009). Empirical likelihood test for a class of regression models. Bernoulli, 15, 955-976.[pdf] 
26 Wang, D. and S.X. Chen (2009). Empirical Likelihood for  Estimating Equation with Missing Values. The Annals of Statistics, 37, 490_517.[pdf] 
27Wang, D. and Chen, S. X. (2009). Combining quantitative trait loci analyses and microarray data, an empirical likelihood approach. Computational Statistics and Data Analysis, 53, 1661_1673.[pdf] 
28Chen, S.X. and Chiumin Wong (2009). Smoothed Block Empirical Likelihood for Quantiles of Weakly Dependent Processes, Statist Sinica, 19, 71-82.[pdf] 
29 Chen, S. X., L. Peng and Y-L, Qin (2009). Effects of Data Dimension on Empirical Likelihood, Biometrika, 96, 711_722.[pdf] 
30 Chen, S. X. and P-S Zhong (2010). ANOVA for longitudinal data with missing values. The Annals of Statistics, 38, 3630-3659.[pdf] 
31 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] 
32 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 
33 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]