陈松蹊，男，1961年11月出生于北京市，数学家，统计学家，中国科学院院士，清华大学统计学研究中心、清华大学统计与数据科学系教授、讲席教授。

1961年11月，陈松蹊出生于北京市。

1983年，毕业于北京师范大学，获数学学士学位。

1988年，毕业于北京师范大学，获数学硕士学位。

1990年，毕业于惠灵顿维多利亚大学，获统计与运筹学硕士学位。

1992年—1995年，任澳大利亚联邦科学院（CSIRO）海洋实验室统计师。

1993年，毕业于澳大利亚国立大学，获统计学博士学位。

1995年—2000年，任拉筹伯大学（La Trobe University）讲师、高级讲师（终身教职）。

2000年—2003年，任新加坡国立大学副教授。

2003年—2017年，任爱荷华州立大学（Iowa State University）统计系终身副教授、教授。

2008年，任北京大学教授、讲席教授。

2008年—2013年，任北京大学商务统计与经济计量系系主任。

2010年，创立北京大学统计科学中心，任北京大学统计科学中心首届联席主任。

2014年—2021年，任北京大学商务统计与经济计量系系联合系主任。

2021年8月1日，入选2021年中国科学院院士增选初步候选人名单；11月18日，当选中国科学院院士。

2023年2月23日，受聘为江西财经大学统计学科首席科学家。

2024年6月3日，受聘为西南财经大学统计交叉创新研究院首席科学家。

2024年，任清华大学统计学研究中心教授。

2024年12月，辞任新城控股集团股份有限公司独立董事。

陈松蹊以国家大气污染防治的重大需求为出发点，在数学地球物理领域做出了前沿交叉成果，为精准度量污染排放和评估大气治理效果提供了科学方法。

陈松蹊与合作者提出了基于U-统计量和L2范数的超高维均值向量、协方差矩阵和回归系数的假设检验方法，突破了已有检验均要求数据维数和样本量是同阶的限制，在超高维下实现了对假设检验第一类错误概率的控制。在几个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做出了贡献。

据2024年1月北京大学光华管理学院网站显示，陈松蹊在学术杂志发表论文126篇。Web of Science H-指数 31，I-10指数56， 总他引3127次。

[1] Gu, J. and Chen, S.X. (2024) Distributed Statistical Inference under Heterogeneity, Journal of Machine Learning Research to appear .

[2] Zheng，Xiangyu and Chen，S.X. （2023）Segmented Linear Regression Trees，Acta Mathematica Sinica，to appear.

[3] Chen, Hanyue, Chen, S.X. and Mu Mu (2023). A Statistical Review on the Optimal Fingerprinting Approach in Climate Change Studies, Climate Dynamics, to appear.

[4] Tong, P.F., Chen, S.X. and Tang, C.Y. (2023) Multivariate calibrations with auxiliary information, Statistica Sinica, to appear.DOI:10.5705/ss.202023.0151

[5] Zheng, Xiangyu and Chen, S.X.(2023) Dynamic synthetic control method for evaluating treatment effects in auto-regressive processes.Journal of the Royal Statistical Society Series B: Statistical Methodology,00:1–22.

[6] Chen, S.X., Qiu, Y.M. and S.Y. Zhang (2023) Sharp Optimality for High Dimensional Covariance Testing under Sparse Signals, The Annals of Statistics,51(5):1921-1945.

[7] Tong, P.F., Zhan, H.X. and Chen, S.X. (2023) Ensembled Seizure Detection based on Small Training Samples, IEEE Transaction on Signal Processing,72: 1-14. DOI：10.1109/TSP.2023.3333546

[8] Zhang, SY, S.X. Chen and Yumou Qiu (2023) Mean Tests For High-dimensional Time Series, Statistica Sinica, to appear.

[9] Peifeng Tong, Wu Su, He Li, Jialin Ding, Haoxiang Zhan, Song Xi Chen (2023). Distribution Free Domain Generalization, Proceedings of the 40th International Conference on Machine Learning(ICML).

[10] Ying Zhang, Song Xi Chen, Le Bao(2023). Air pollution estimation under air stagnation—A case study of Beijing,Environmetrics,34(6), e2819

[11] Zhu Y, Gu J, Qiu Y, Chen SX. （2023）Real-World COVID-19 Vaccine Protection Rates against Infection in the Delta and Omicron Eras. Research，6，Article 0099.

[12] Zhu，YR, Gu, J., Yumou Qiu, S.X. Chen (2023) Estimating COVID-19 Vaccine Protection Rates via Dynamic Epidemiological Models--A Study of Ten Countries, The Annals of Applied Statistics,17(4):3324–3348.

[13] Chen，SX, Guo, B. and Qiu, YM (2023) Testing and Signal Identification for Two-sample High-dimensional Covariances via Multi-level Thresholding, Journal of Econometrics, 235, Issue 2, 1337-1354.

[14] Tong, P. F., Chen, S. X., & Tang, C.Y. (2022). Detecting and evaluating dust-events in North China with ground air quality data. Earth and Space Science, 9, e2021EA001849

[15] Luo, S., Zhu, Y., & Chen, S. X. (2022). Episode based air quality assessment. Atmospheric Environment, 285, 119242.

[16] 陈松蹊，毛晓军，王聪 （2022）大数据情境下的数据完备化：挑战与对策。 管理世界，2022年第1期，196-206.

[17] Li, S-M, Liu, R., Wang, S. and S.X. Chen (2021). Radiative Effects of Particular Matters on Ozone Pollution in Six North China Cities, Journal of Geophysical Research, Vol.126, No. 24, e2021JD035963。

[18] Huang, YX., B. Guo, H. Sun, H. Liu and S. X. Chen(2021) Relative Importance of Meteorological Variables on Air Quality and Role of Boundary Layer Height, Atmospheric Environment,267,118737.

[19] 王振中, 陈松蹊, 涂云东 (2021)，中国居民消费价格指数的动态结构研究及中美量化比较, 数理统计与管理，12（01）：18。

[20] 顾嘉 , 陈松蹊, 董倩, 邱宇谋 （2021）基于vSEIdRm模型的人口迁移以及武汉封城对新冠肺炎疫情发展的影响分析，统计研究，Vol.38, No.9。

[21] Yan, H., Zhu, YR., Gu, J., Huang, YX., Sun, HX., Zhang, XY., Wang, YT., Qiu, YM. and Chen, S.X. (2021). Better strategies for containing COVID-19 pandemic: a study of 25 countries via a vSIADR model, Proceedings of the Royal Society A, 476: 20200440.

[22] Zhu, Y.R.，Liang, Y.S. and Chen, S.X. (2021) Assessing Local Emission for Air Pollution via Data Experiments, Atmospheric Environment, 252, 118323.

[23] [104] Chen, S.X. and L-H Peng (2021) Distributive statistical inference for massive data, The Annals of Statistics, 49, 2851–2869.

[24] Chang, J-Y., Chen, S.X., Tang, C-Y. and Wu, T-T (2021) High-dimensional empirical likelihood inference, Biometrika, 108, 127-147.

[25] Zhang, HM and Chen, S. X. (2021), Concentration Inequalities for Statistical Inference (Review Paper), Communications in Mathematical Research, 37, 1-85. doi: 10.4208/cmr.2020-0041

[27] Zheng, 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), Envirionmentrics, Volume 32, Issue 2，e2673.

[28] 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》, 第26卷第2期。

[29]Zhang, S., Chen, S.X. and Lu, L. (2021), Inference for Variance Risk Premium, China Finance Review International, 11, 26-52.

[30] Mao, X-J., Wong, R. K-W and Chen, S. X. (2021) Matrix Completion under Low-Rank Missing Mechanism, Statistica Sinica, 31, 2005-2030.

[31]Wu, 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.

[32]Wan, Y., Xu, M., Huang, H. and Chen, S.X. (2020) A spatio-temporal model for the analysis and prediction of fine particulate matter concentration in Beijing, Enviromentrics, 32 (1), e2648.

[33]Haoxuan Sun, Yumou Qiu, Han Yan, Yaxuan Huang, Yuru Zhu, Jia Gu and Song Xi Chen(2020) Tracking Reproductivity of COVID-19 Epidemic in China with Varying Coefficient SIR Model (with discussion)，Journal of Data Science 18 (3), 455–472.

[34]Ziping 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.

[35] Shuyi Zhang, Song Xi Chen, Bin Guo, Hengfang Wang, Wei Lin (2020) Regional Air-Quality Assessment That Adjusts for Meteorological Confounding, Science China Mathematics, 50, 527-558.

[36]Gu, J., Yan, H., Huang, J., Zhu, Y., Sun, H., Qiu, Y. and S. X. Chen（2020）, Comparing Containment Measures among Nations by Epidemiological Effects of COVID-19. National Science Review, 7: 1847–1851. doi: 10.1093/nsr/nwaa243.

[37] Zheng, XY and Chen, SX (2019) Partitioning Structure Learning for Segmented Linear Regression Trees, Advances in Neural Information Processing Systems (NeurIPS), 2019.

[38] Mao, 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

[39] Chen, S.X., Li, J. and P.-S. Zhong, (2019) Two-Sample and ANOVA Tests for High Dimensional Means, The Annals of Statistics, 47, 1443-1474.

[40] Li, 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.

[41] J. He and S. X. Chen (2018) High-Dimensional Two-Sample Covariance Matrix Testing via Super-diagonals, Statistica Sinica, 28, 2671-2696.

[42] Chen, L., Guo, B., Huang, J, He, J., Wang, H., Shuyi Zhang, and S.X. Chen (2018). Assessing air-quality in Beijing-Tianjing-Hebei region: the method and mixed tales of PM2.5 and O3. Atmospheric Environment, 193, 290-301.

[43] Qiu, Y., Chen, S.X. and Nettleton, D.（2018）Detecting Rare and Faint Signals via Thresholding Maximum Likelihood Estimators, Annals of Statistics, 46, 895-923.

[44] Zhang, SY, Guo, B. Dong, A., He, J., Xu, Z and Chen, SX (2017) Cautionary Tales on Air Quality Improvement in Beijing, Proceedings of the Royal Society A, 473: 20170457.

[45] Zuo, T. and S. X. Chen (2017). Enhancing Estimation for Interest Rate Diffusion Models with Bond Prices. Journal of Business and Economics Statistics, 35:3, 486-498.

[46] Guo, B. and S.X.Chen (2016). Tests for High Dimensional Generalized Linear Models. Journal of the Royal Statistical Society, Series B. to 1079-1102.

[47] Wang, Y., Tu, Y-D and S. X. Chen (2016) Improving inflation prediction with the quantity theory. Economics Letters, 149, 112-115.

[48] Chen, S.X. (2016) 　Peter Hall's Contribution to the Bootstrap, The Annals of Statistics, 44, No. 5, 1821–1836.

[49] 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(17), 10220–10236.

[50] 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.

[51] He, J. and S. X. Chen (2016) Testing Super-Diagonal Structure in High Dimensional Covariance Matrices, Journal of Econometrics, 194, 283-297

[52] 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.

[53] Liang, X., T, Zuo, 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.

[54] 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.

[55] 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.

[56] 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.

[57] Chen, S.X. and Z. Xu (2013). On smoothing estimation for seasonal time series with long cycles, Statistics and Its Interface, 6, 435-447.

[58] Chen, S. X., Peng, L. and C. L. Yu (2013). Parameter Estimation and Model Testing for Markov Processes via Conditional Characteristic Functions, Bernoulli, 19, 228-251.

[59] Chen, S. X. and Van Keilegom, I. (2013). Estimation in semiparametric models with missing data. Annals of the Institute of Statistical Mathematics, 65, 785-805.

[60] Chen, S. X., Tang, C.Y. and J. Qin (2013). Mann-Whitney Test with Adjustments to Pre-treatment Variables for Missing Values and Observational Study, Journal of the Royal Statistical Society, Series B., 75, 81-102.

[61] 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.

[62] Li, J. and S. X. Chen (2012). Two Sample Tests for High Dimensional Covariance Matrices, The Annals of Statistics, 40, 908-940.

[63] 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.

[64] Chen, S. X. and C. Y. Tang (2011). Nonparametric Regression with Discrete Covariates and Missing Value. Statistics and Its Interface, 4, 463-474.

[65] J. Chang and S.X. Chen (2011). On the approximate maximum likelihood estimation for diffusion processes. The Annals of Statistics, 39, 2820-2851.

[66] Chen, S. X. and C. Y. Tang (2011). Properties of Census Dual System Population Size Estimators. International Statistical Review, 79, 336-361.

[67] 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.

[68] 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.

[69] Alzghool, R., Y-X Lin and S. X. Chen (2010). Asymptotic Quasi-likelihood Based on Kernel Smoothing for Multivariate Heteroskedastic Models with Correlation, American Journal Of Mathematical And Management Sciences, 30, 147-177.

[70] Chen, S. X. and P-S Zhong (2010). ANOVA for longitudinal data with missing values. The Annals of Statistics, 38, 3630-3659.

[71] 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.

[72] Chen, S. X., Delaigle, A. and Hall, P. (2010). Nonparametric estimation for levy-type processes, Journal of Econometrics, 157, 257-271.

[73] 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.

[74] Chen, S. X., C. Y. Tang and V. T. Mule Jr. (2010). Local Post-Stratification in Dual System Accuracy and Coverage Evaluation for US Census, Journal of the American Statistical Association, Application & Case Studies, 105, 105-119.

[76] Chen, S. X. and I. Van Keilegom (2009). A review on empirical likelihood for regressions (with discussions), Test, 3, 415-447 .

[77] Chen, S. X. and Van Keilegom, I. (2009). Empirical likelihood test for a class of regression models. Bernoulli, 15, 955-976.

[78] C. Y. Tang and S. X. Chen (2009). Parameter estimation and bias correction for diffusion processes. Journal of Econometrics, 149, 65—81.

[79] Chen, S. X., L. Peng and Y-L, Qin (2009). Effects of Data Dimension on Empirical Likelihood, Biometrika, 96, 711–722.

[80] Wang, D. and S.X. Chen (2009). Empirical Likelihood for Estimating Equation with Missing Values. The Annals of Statistics, 37, 490–517.

[81] Wang, 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.

[82] Chen, S.X. and Chiumin Wong (2009). Smoothed Block Empirical Likelihood for Quantiles of Weakly Dependent Processes, Statist Sinica, 19, 71-82.

[83] Chen, S. X., Leung, D. Y. H. and J. Qin (2008). Improved Semiparametric Estimation Using Surrogate Data. Journal of the Royal Statistical Society, Series B, 70, 803-823.

[84] Chen, S.X., J. Gao and C. Y. Tang (2008). A Test for Model Specification of Diffusion Processes. The Annals of Statistics, 36, 167-198.

[85] Chen, S.X: (2008). Nonparametric Estimation of Expected Shortfall. Journal of Financial Econometrics, 6, 87-107.

[86] Chen, S. X. and T. Huang (2007). Nonparametric Estimation of Copula Functions for Dependent Modeling. Canadian Journal of Statistics, 35, 265-282.

[87] 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.

[88] Chen, S.X. and J. Gao (2007). An Adaptive Empirical Likelihood Test For Time Series Models, paper, full report, Journal of Econometrics, 141, 950-972.

[89] Chen, S.X. and H.-J., Cui (2006). On Bartlett Correction of Empirical Likelihood in the Presence of Nuisance Parameters, Biometrika, 93, 215-220.

[90] Chen, S.X. and Qin, J. (2006). An Empirical likelihood Method in Mixture Models with Incomplete Classifications, Statistica Sinica,16, 1101-1115.

[91] Chen, S. X. and Tang, C. Y. (2005). Nonparametric Inference of Value at Risk for dependent Financial Returns. Journal of Financial Econometrics, 3, 227-255.

[92] 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.

[93] Chen, 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.

[94] Chen, S. X. and Qin, J. (2003). Empirical likelihood based confidence intervals for data with possible zero observations. Statistics and Probability Letters, 65, 29-37.

[95] Chen, 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.

[96] Chen, S. X. and Hall, P. (2003). Effects of bagging and bias correction on estimators defined by estimating equations, Statistica Sinica,13, 97-109.

[97] Chen, S. X and Cui, H-J. (2003). An extended empirical likelihood for generalized linear models. Statistica Sinica, 13, 69-81.

[98] Chen, S. X. and Hall, P. (2003). EFFECTS OF BAGGING AND BIAS CORRECTION ON ESTIMATORS DEFINED BY ESTIMATING EQUATIONS, Statistica Sinica, 13, 97-109.

[99] 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.

[100] Chen, S. X, Yip, P. and Zhou, Y. (2002). Sequential line transect surveys. Biometrics, 58, 263-269.

[101] Chen, S. X. (2002). Local linear smoothers using asymmetric kernels. Ann. Inst. Statist. Math., 54, 312-323.

[102] Chen, S. X. and Lloyd, C. J.(2002). Estimation of population size based on biased samples using nonparametric binary regression. Statist. Sinica, 12, 505-518.

[103] Chen, S. X. and Qin, Yong Song (2002). Confidence interval based on a local linear smoother. Scand. J. Statist., 29, 89-99.

[104] Chen, S. X. and Cowling, A. (2001). Measurement Errors in Line Transect Surveys where Detection varies with Distance and Size. Biometrics, 57, 732-742.

[105] Chen, S. X. and Qin, Yong Song (2000). Empirical Likelihood confidence interval for a local linear smoother. Biometrika, 87, 946-953.

[106] Chen, S. X. and Lloyd, C. J. (2000). A non-parametric approach to the analysis of two stage mark-recapture experiments.Biometrika, 87, 633-649.

[107] Chen, S. X. (2000). Gamma kernel estimators for density functions. Ann. Inst. Statist. Math. 52, 471-480.

[108] Chen, S. X. (2000). Animal abundance estimation for independent observer line transect surveys. Special Issue of Environmental and Ecological Statistics: Statistical Ecology and Forest Biometry 7, No. 3, 285-299.

[109] Chen, S. X. (2000). Beta kernel smoothers for regression curves. Statistica Sinica.10, 73-91.

[110] Chen, S. X. (1999). Beta kernel estimators for density functions. Computational Statistics and Data Analysis, 31, 131-145.

[111] Chen, S. X. and Woolcock, J. (1999). A condition for designing bus-route type access site surveys to estimate recreational fishing effort. Biometrics. 55, No. 3, 799-804.

[112] Chen, S. X. (1999). Estimation in independent observer line transect surveys for clustered populations. Biometrics, 55 , No. 3, 754-759.

[113] Brown, B. M. and Chen, S. X. (1999). Beta-Bernstein smoothing for regression curves with compact support. Scand. J. Statist. . 26, 47-59.

[114] Brown, B. M. and Chen, S. X. (1998). Combined Empirical Likelihood. Ann. Inst. Statist. Math, 50, 697-714.

[115] Chen, S.X. (1998). Measurement errors in line transect surveys. Biometrics, 54, 899-908.

[116] Chen, S.X. (1997). Empirical likelihood for nonparametric density estimation. Aust. J. Statist. , 39,47-56

[117] Chen, S.X. and Polacheck, T. (1996). Kernel estimates of mean school size for IWC minke whale data. Report of International Whaling Commission, 46, 341-348.

[118] Chen, S.X. (1996). Empirical likelihood confidence intervals for nonparametric density estimation. Biometrika, 83, 329-341.

[119] Chen, S.X. (1996). Studying school size effects in line transect sampling using the kernel method. Biometrics , 52, 1283-94.

[120] Chen, S.X. (1996). A kernel estimate for density of a biological population using line transect sampling. Royal Statistical Society Ser. C: Applied Statistics, 45, 135-150.

[121] Chen, S.X. (1994). Comparing empirical likelihood and bootstrap hypothesis tests. J. Mult. Anal, 51, 277-293.

[122] Chen, S.X. (1994). Empirical likelihood confidence intervals for linear regression coefficients. J. Mult. Anal. 49, 24-40.

[123] Chen, S.X. and Hall, P. (1994). On the calculation of standard error for quotation in confidence statements. Statistics and Probability Letters,19,147-151.

[124] Chen, S.X. and Hall, P. (1993). Smoothed empirical likelihood confidence intervals for quantiles. Ann. Of Statistics, 21,1166-1181.

[125] 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.

[126] Chen, S.X., Smith, P.J., Shafi, M. and Vere-Jones, D. (1990). Some improvements to conventional importance sampling techniques for coded system using Viterbi decoding. Electronics Letters, 26, 802-806.

陈松蹊主讲课程：高等多元统计分析、大样本统计理论。

博士研究生：王莹（中国人民大学经济学院教师）、 张澍一

硕士研究生：孙浩轩

陈松蹊认为做研究一定要保持一个向上的心态，保持积极的心态，要有强烈的内驱力、有耐性。

毕业生要确立更高的目标，同时正确看待人生中的曲折和弯路，享受不确定性带来的可能性和惊喜，在困难面前永不放弃，努力为推动社会进步贡献北师大人的智慧。

陈松蹊从不停留在舒适区，而是会选择内心深处认为真正重要的问题，凭借着学术直觉与热情信念，全身心地投入到新理论与新领域的开拓中去。（北京大学评）